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Welcome to the Chronos Fusion Energy podcast.

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I'm Priyanka Ford, the founder of Chronos Fusion Energy, and today I'm thrilled to be

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joined by Paul Weiss, our chief material scientist and board advisor.

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Paul is an extraordinary nanoscientist and material science expert with an impressive

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career that spans decades.

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He's currently affiliated with the University of California, Los Angeles, where he holds

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a chair position in the nanosystem sciences and served as the director of the California

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Nanosystems Institute.

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At Chronos Fusion Energy, Paul brings invaluable insights into the world of materials, and

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his work is crucial for our fusion energy technology.

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Paul has co-authored over 400 research publications and holds more than 40 U.S. and international

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patents.

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His academic journey started at the Massachusetts Institute of Technology, MIT, where he earned

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his bachelor's and master's of science degrees, and continued to the University of California

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in Berkeley, where he completed his Ph.D. in chemistry.

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He has worked at Bell Labs, IBM Research, and spent 20 years at Pennsylvania State University,

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climbing the ranks from assistant professor to distinguished professor of chemistry and

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physics.

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Along the way, Paul has received numerous awards, including the IEEE Pioneer Award in

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nanotechnology and election into the American Academy of Sciences and Arts.

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In this episode, we dwell into Paul's journey from MIT to his current roles at UCLA, Harvard,

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and Chronos Fusion Energy.

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We'll explore his fascination with nanotechnology, his breakthrough work with the scanning tunneling

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microscope, and how his research is driving the material science innovations crucial to

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our smart fusion energy generator.

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We'll also discuss his role of nanotechnology in various fields, from electronics to medicine,

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and how AI and quantum computing might influence future developments in material science.

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Paul was my very first partner at Chronos.

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Join me as we unravel the incredible story of Paul Weiss, a pioneer in nanoscience, and

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gain insights into the breakthroughs that could shape the future of fusion energy.

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Here's my co-founder, Paul Weiss.

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Hi, Paul.

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Thank you so much for doing this.

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I think out of all the people that work at our company, when I talk about you, I think

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people are really most curious because you just have so much experience in the material

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sciences world.

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You started at MIT.

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What got you into nanotechnology and material science, Paul?

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Well, when I went to MIT, there really wasn't nanoscience yet.

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My interests there were piqued by work I was doing in chemistry, where I wanted to understand

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how chemistry and electronic structure were coupled.

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That turned into a career goal that I thought would take my entire career.

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Really early on, once I was an independent researcher, we were able to do what I intended

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to take a lifetime to do every day in the laboratory, later leading to a crisis.

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What I really liked about MIT was all the doors were open to undergraduates.

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When I had some interest in something, I would just go look up who was the expert in it.

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When I walked into their office, they would spend an hour with me.

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If there was something that we could actually do on a problem like that, for instance, I

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wanted to digitize data and there was no equivalent, no technology to do that.

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I worked with someone who was early in digital photography.

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We turned the traces on the paper into digital data that we could use.

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That just came from going to someone who worked in that area.

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He said, well, that sounds like an interesting problem.

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Then we proceeded.

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I learned not to be shy about asking for help from the top experts in the world.

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I would say that continues to this day.

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Yeah.

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It's always good to have educational resources you can reach out to.

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Yeah, I felt very privileged to have that with my professors.

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Do you feel the need to be the same way with your students?

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Absolutely.

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I'm at UCLA and the culture here is very open door.

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I expect my students to go to other faculty and start a conversation, sometimes start

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a collaboration.

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I do the same with others who come around to my office.

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In fact, as you know, we run this initiative where we take on unsolved problems and then

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put teams together to go after them.

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Part of the fun there is when we think of an idea and start to pursue it, we call up

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the top engineers in the world in whatever field it is, whether they're in Harvard or

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Stanford or Singapore or wherever else.

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A hundred percent of the time, they agree to work with us.

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We've been able to move very quickly because we're not reinventing what our friends and

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colleagues and collaborators have already done.

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We pique their interest with the new problems that we're going after and what the impact

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of solutions might be.

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I think that really dates back to my time as an undergraduate and the opportunities

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that I had there.

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I didn't see that graduate students got the same deal for some reason there, but it's

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something that's been in my mind.

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At the places where that was the culture, I've been much happier.

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I'll just leave it at that.

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Nice.

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Yeah.

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How does chemistry and digitization then lead you to nanotechnology?

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What does that mean?

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My longer trajectory was that I did spectroscopy in a laboratory of a professor I had for two

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or three courses.

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I ended up jumping into courses that were way over my head that he was teaching and

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then learning all I needed to do since I didn't have the prerequisites and later helping teach

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those classes.

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What interested me led me to my PhD work at Berkeley where I was looking at the reactions

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of excited atoms.

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I learned, I would say, a couple of negative lessons there in that it was a way too specific

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approach to the problem.

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We learned some interesting things and those are textbook experiments now, but it didn't

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afford me the kind of creativity I like where I could choose each day what it is I wanted

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to do in the laboratory and what I might learn.

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I realized I needed a more general solution and I needed to be in a richer environment.

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When I was a PhD student, we didn't have the culture of going to other groups.

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We had a lot of really smart people in our group who were professors all over the world

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now, but it was fairly narrow.

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I moved from there to Bell Laboratories thinking that we could manipulate the electronic structure

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of semiconductor surfaces, that that would be a general approach.

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But nobody was doing that at the time in the context of chemistry, so I found the closest

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thing that I could.

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There was a terrific scientist there named Mark Cardillo who in many ways acts and looks

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and talks like Danny DeVito, so he was also a lot of fun.

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What he was doing was exciting semiconductor surfaces from collisions with atoms that didn't

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react and then we moved into reactions.

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We figured out that we could measure just a tiny fraction of a reaction on a surface

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and we were very sensitive to that using the same physics that's involved in the semiconductor

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industry, essentially what leads to transistors.

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Those were in fact invented at Bell Laboratories and some of the key figures were still walking

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around the halls and having lunch with us at the time.

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What we didn't know was what was on the surface and where it was.

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The scanning tunneling microscope had just been invented at that time and almost all

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the work was being done at Bell Laboratories and IBM.

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The microscope had been invented at IBM.

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The inventors won the Nobel Prize in 1986, which turned out to be the year that I started

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my postdoc at Bell.

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There was an opportunity to measure where things were and there was another postdoc

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at Bell named Don Eidler who when he finished his training position there, he moved out

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to IBM in San Jose to the Almondon Laboratory with the idea of measuring what was on the

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surface as well using something called vibrational spectroscopy.

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There was an idea of how you could do that for a single molecule on the surface so you

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could figure out where it was, what its environment was, and then what it was.

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That experiment turned out to be hard.

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In fact, we worked on it for 13 years before anybody made it work.

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I just came back from the American Chemicals side in New Orleans and the person who did

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actually succeed first, Professor Wilson Ho who was at Cornell when he did those experiments

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now at UC Irvine not too far from here, showed that one could actually make that work.

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That was the intriguing piece that sucked me into nanoscience in that I would be able

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to visualize the chemistry.

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That's the right way to put it.

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We could measure structure and we could measure what are called spectra.

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Later in my laboratory we also developed a whole series of spectroscopies where we could

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also measure the function of single molecules and precise assemblies of molecules mimicking

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what happens in our cells in biology.

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Would you say this time was an era that was like, what do we say?

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Is it the same for computing the excitement, the precipice of something new being on the

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horizon?

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Is that the same feeling that we now have with quantum computing?

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I think nanoscience is probably broader actually.

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There was so much unexplored territory that every time we went in the laboratory we discovered

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something new.

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I think in quantum computing we're trying to figure out what the best implementation

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of it will be and then much of what one could possibly do with it has been I think described

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theoretically.

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Figure those two sides of how far can we take it both experimentally and where will we really

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be able to use quantum computers to move forward.

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I think in nano it was almost like going into an unexplored world that 99% of the time we

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would find things we didn't understand and we'd pick off one in 10 of those to go after

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and one in 10 of the ones we decided to pursue we would figure out and that was plenty to

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really make a mark in the field.

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I think there's so much effort and overlap between groups in quantum computing it's hard

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to have the same kind of breakthroughs.

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It's a little bit more of a race from one group and approach to another at the moment.

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Right, yeah, material science is so vast Paul.

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Somebody recently told me about the behavior of graphene and basically we know that it

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works but we don't really know how it works and this was like an incredibly smart person

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so that scared me.

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Is that true?

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I would actually say it kind of went the other way around so there was this amazing scientist

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named Millie Dresselhaus and I actually get an interview with her for ACS nano and she

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was on my board.

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She's unfortunately since passed away but she worked out a lot of what the properties

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of graphene and other carbon materials would be if anyone were ever to make it and she

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really worked independently if you do read that interview when she got her PhD at University

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of Chicago the person in charge of the physics graduate program didn't believe that women

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should get PhDs so she was left on her own and Enrico Fermi kind of adopted her.

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She was the same age as his daughter so he had her over to dinner once a week and encouraged

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her and then when she went to Cornell for a postdoc she had a fully funded postdoc but

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her husband had a job at Cornell and due to the nepotism laws there she couldn't be hired

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and so again she worked independently and her career kind of went on like that.

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It was a real lesson in persistence where she thought this was an important area and

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the world eventually came around to her and it was part of the reason people called her

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the queen of carbon because she figured out what those properties would be in theory before

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the materials were ever developed and you know when our friends Kostya Novoselov and

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Andre Geim ultimately showed that you know what we were doing to make clean graphite

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all the time which was pulling off layers with the scotch tape they looked at the scotch

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tape side and thought that they could get individual layers and then they worked out

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all the properties because they had that material literally in their hands.

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People had actually synthesized single layers of carbon on metals you know some years earlier

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and described them and there were even scanning telemicroscope images of them.

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John Heminger at UC Irvine for instance and one of my colleagues Rick Koenner had made

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some materials as well but to your point I think the theoretical a lot of the theory

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was understood and then once the materials were there we could go much much further with

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them and to this day people continue there we published a paper on two layers of graphene

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that are twisted and their properties just in the last week.

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Yeah yeah wow are there a lot of materials like that that we don't know of do you think?

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Well there are certainly families of materials that we're opening up so again one of the

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parts of this meeting in New Orleans was looking at what are called maxines so a new class

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of 2D materials that are a friend and colleague and collaborator Yuri Gagatsi at Drexel discovered

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and so that has led to thousands of different materials.

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There are other you know two there are many materials in nature that are that are layered

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graphene comes from graphite which you can dig out of the ground that was what was you

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know used for pencils for many years and still there are other materials like well one mineral

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is molybdenite it's probably the second most common layered material it's another one where

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you can just peel the layers off with scotch tape if you want to look at individual layers

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or people have developed ways to grow most of these now but there are still many many

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materials being discovered and those you know one can target such materials that then have

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interesting and novel properties where you mix for instance dimensionality so some things

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you know 1D and 2D or 2D and 3D at the same time and by modulating you know back and forth

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between the two then you get something very interesting.

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Is that the kind of data that was fed into that Google AI that created I think it created

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like 380,000 material compositions in a couple of weeks and then it was like 800 years worth

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of research is that how it does that then it just looks at synthesis of different elements

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combined together in different proportions and what they can do?

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What we need to learn from AI we'll maybe start with that is a lot of data so that what

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you can do really depends upon the data set that's why people are feeding so much into

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you know chat gbt and other you know other programs like that it's the same thing with

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materials if you have a good enough data set then you can interpolate within it what I

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don't think AI does for you is extrapolate to something that's very new so you might

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be able to let's say tune the properties to make an extremely efficient catalyst and you

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know tell synthetic chemists or material scientists look here and it's you know sort of point

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and treasure map kind of way to what you should be targeting and then gather more data specifically

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around what looks like an optimized structure but what it won't do I think is jump out of

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the known data set into something that doesn't exist at all.

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I wonder about that.

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We'll see.

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Yeah we'll see because then I feel like in the future wouldn't you be able to give it

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parameters like for just to connect it with our fusion energy industry like if you could

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tell it hey find a way to harness neutrons or something and then you know we don't have

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to worry about that so much we have to worry about a little bit of neutrons and a little

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bit of radiation but it's just would it be able to do it would it be able to build a

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material for a purpose without any given parameters and just like an open data set in that just

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go find the data you need.

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I think that that's the key point the data have to exist in order to be fed into that

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program so it won't jump out at you but you know for things like you know nuclear reactions

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where we know their you know we know the cross sections and energies and everything else

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I would call that known and some of the data doesn't have to be experimental data can be

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theoretical data as long as it's trustworthy and if there are flaws in it then those flaws

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will be reproduced in the results of the you know hopefully people figure out how to how

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to rate the trustworthiness of data as one goes along because we face the same thing

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you know when we don't use AI if we trust some data that turns out to be faulty or erroneous

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or even off by a little bit that can affect you know our not only our results but our

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direction.

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How far has nanotechnology gotten like can are we at a point where we can use it for

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surgeries?

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Are we like five years away from that?

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Well we have I mean it's in every phone and device and computer you have all the all the

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chips that are are there made with nanotechnology the if you get a quantum dot television that's

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a nanotechnology if you got a COVID vaccine that was nanotechnology so I would say they're

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already in us and that is one of the key features that of nano and part of its importance is

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that's the scale of function in biology so the if you look at the synapse scale in the

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brain my wife as you know is a neuroscientist that's a 10 or 20 nanometer gap across the

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synapse and you know her her comment to me all along has been well the brain's been nano

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for hundreds of millions of years you people are just slow and and we what the advantage

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we have is because we've learned to control materials at the nanoscale we can interact

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at the functional scale in biology and that means both readout and you know that was reflected

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in the brain initiative that was based largely on her ideas when she left a National Institute

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of Mental Health in terms of listening in on chemical communication in the brain but

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then also you know directing function and so you know drugs like a Braxane are based

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on that's a protein nanoparticle that carries powerful anti-cancer drug a taxol and it's

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you know simple but it it leads to greatly reduced side effects based on you know the

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the formulations that weren't nano previously and so going you know further one can imagine

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designing interactions that led us target a particular part of the body or particular

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tumor or taking advantage of things like leaky vasculature and tumors or slightly more acidic

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environment you know in in the way nanolithography is done in the semiconductor industry one

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of the keys to get much higher resolution than the diffraction limit of light which

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was thought to be what was going to limit the scale of devices is to use acid-based

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catalysis and polymers and so we can turn reactions on based on pH and we can do the

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same thing in the body if there's a different pH for instance there's usually a more acidic

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environment in tumor so we can try to turn drugs on or release them in the same way using

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those internal variations and if you can do that then you know there are do side effects

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for example and there's a there's a big community in nanomedicine that tries to exploit those

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possibilities.

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Yeah the big applications I've seen with quantum computer quantum computing has been medicine

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research and algorithmic trading I've used the IBM's Watson interface for quantum algorithms

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with algorithm trading like almost five years ago it's been around yes but the other area

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that it's really been kind of around and impacting has been medicine and it's probably because

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of like the nanoscale of it all and the huge amounts of data that makes sense.

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Yes yeah one time there was a discussion you know that that okay we're done with nano we're

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going to move up to a bigger scale meso and then you know the argument against that was

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well what don't you want to know that the nanoscale do you not want to know the DNA

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sequence or you don't want to know you know what the chemistry on the outside of the virus

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is there's you don't just because you moved to a bigger scale it doesn't allow you to

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ignore those smaller scales so when you're targeting the smaller scales and I think the

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nice thing for where quantum computing is now is we have what will seem like toy systems

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compared to what we'll have in the future and so we're able to work out algorithms we'll

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be able to work out what it is we will be able to do by looking at relatively small

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you know at least what will seem like relatively small calculations now compared to what's

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going to happen when we have more qubits in the in the in the quantum computers it's an

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exciting time in that way and it's evolving very quickly and we have we can get our students

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on quantum computers and have them do some some calculations to gain some familiarity

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but that'll also get them thinking about what will they be able to do when the power increases

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what is that so where will what will be we be doing in terms of nanotechnology like 15

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years from now that sounds infathomable to us today well put it this way we used to have

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group meetings where we would try and come up with things that we couldn't do for some

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fundamental reason and ultimately you know we didn't always figure out how we could do

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something but we didn't really come up with anything that we would not be able to do in

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terms of a measurement and or precise control I mean even now we can and this is part of

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how and my neuroscientist wife and I got together was we learned to control the placement of

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molecules from a fraction of a molecule all the way up to centimeters so we kind of added

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that chemical dimension to nanolithography if you will that opened up control of the

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control and interactions with the biological world so I don't see a I don't really see

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a limit there what we you know when you mentioned medicine nanomedicine one of the you know

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one of the things that one has to do I think to be realistic about it is think about what's

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going to get through regulatory approval and for that you have to understand how everything

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you make is metabolized in the body and so for that reason you know that's an argument

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for simplicity so that so that you can move forward and actually have impact clinically

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as opposed to just sticking a bunch of nano things together and saying well this one does

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that and this one does that I ran a journal for many years and we had a clinician and

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other people really involved in medicine who you know we decided together we just would

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never publish papers like that that would never have any chance of of getting inside

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a human just because of their their complexity they could get a lot of attention for the

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papers but they were not meaningful in terms of of actual medicine I don't know if that

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makes sense.

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It does yeah you don't want to just do it for the sake of doing it it has to metabolize

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and actually have a purpose.

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Yeah and that's well there can be a purpose but you also have to know what the products

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are and then what those might do in the body and so there's I think in many cases there's

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an argument for for simplicity because then you can have understanding of the understanding

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the whole system we whenever we do a fair bit of invention we identify problems and

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go after them and we always look for the simplest solutions that are effective and that that

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lets us go much further much faster.

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So are we kind of implying that with computing in the future we'll be able to maneuver those

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unknowns a lot better and make more complex things or is simplicity still going to be

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the way to go?

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I think simplicity still to me the way to go right if even now like chips that we make

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there there can be you know hundreds of layers in the processing that's part of what makes

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fab cost so much.

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Where chips are made right now right TSMC makes most of the complicated chips in the

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world because you can't afford to have too many factories like that.

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There was a plot that went along with Moore's law showing the cost of the fabrication centers

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compared to the GMP of countries.

327
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It's at that scale even to make the chips that go in our laptops and phones.

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Yeah did you when you guys were building that stuff out in at IBM did you think that it

329
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was that we would run into the scarcity we're running into now because of geopolitics?

330
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Well we weren't really on that side we were on you know we were on the side of fundamental

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science but in fact the reason the microscope was developed originally was to look at flaws

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defects in the oxide insulators that people invented didn't expect to get atomic resolution.

333
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I think there wasn't you know as the as the industrial part developed yeah I don't think

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when people foresaw all the supply chain issues and everything else that came along with pandemic

335
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it's and it's not just in it's not just in technology you know many countries now are

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after food security they want local production so for instance in Korea they're building

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an enormous agricultural campus for universities in order to be able to generate food locally

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rather than depend upon others in Singapore right it's a it's a tiny tiny place where

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people right and they're trying to trying to you know if not be self-sufficient at least

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more able to to sustain themselves with what can be grown in you know relatively urban

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environments and so there's been a lot of push across many many different fields for

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okay what could we do to to mitigate the risk of you know depending internationally so much

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on one another.

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Yeah switching gears Paul like you you have quite a few awards here you have the IEEE

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Pioneer Award that's kind of a big deal like you have so many awards what do you try for

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this or do people just look at your work and they seek you out and award you how does this

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work in the academic?

348
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It's a combination of things so you know within it does your institution a lot of good when

349
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there is an award so there's a you know there is a group of of people within the department

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that looks for honors we can put put our colleagues up for but sometimes people just you know

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00:31:26,000 --> 00:31:32,880
read a read your CV online or sometimes very outdated biography in one case and then nominate

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you for an award and the next thing you know the first time you ever hear about is when

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00:31:36,240 --> 00:31:41,960
you get a call saying you know you've won this award please come here so there I mean

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00:31:41,960 --> 00:31:47,360
there's there's a mix some are more kind of lifetime awards and others are more tuned

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to a particular project so for instance my student just won this Collegiate Inventors

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Award for something he developed in our laboratory and my it was kind of fun for me because our

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my very first student had also won that award for inventing some microscopes in the very

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early days in our laboratory.

359
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Were there any awards you knew of as a child that you now have as an adult?

360
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I don't think so I wasn't so there are these I wasn't so aware of these things yeah.

361
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What did your parents do?

362
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My father was professor at Cornell in game theory they started they started as so they

363
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they started a statistics department two weeks after he passed away which would have been

364
00:32:34,320 --> 00:32:38,200
the appropriate one for him but he was part of a group of mathematicians at Columbia where

365
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he'd gotten all his degrees and they the five of them moved to Cornell all the same time

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and then right after they got there they pulled him away from another university and brought

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him in and he never left.

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And mom was mom assigned?

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He was trained as a mathematician they actually met in a math class but she did many things

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over the years during World War II she was an engineer and that and she was one of only

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three people they kept on after the war to continue at Western Electric and then later

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she taught the death and then did did a whole series of other other things in her career

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and she's still still going strong I talked to her this morning living on her own at a

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hundred in the house I grew up in.

375
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That's awesome that's like super line that's awesome.

376
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Did they did they tell you about material science when you were young?

377
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Oh not at all.

378
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My father didn't talk very much unless he was in front of a class he was a very good

379
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he was sort of known as very good teacher and he did all pencil and paper theory he'd

380
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used a computer in the early days and when he was a student but there were no programming

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languages yet so he decided they were useless and then his his PhD students would turn some

382
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of his you know some of his equations into algorithms that they would test but he didn't

383
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really use them till he was retired and decided they were actually useful after all and then

384
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I don't think he he didn't have too much to do with materials that I was aware of until

385
00:34:09,880 --> 00:34:14,640
he passed away I went through his office and I found a manuscript who was actually working

386
00:34:14,640 --> 00:34:21,780
on a problem in surface and nanoscience with one of my colleagues and I I stupidly threw

387
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the partially finished manuscript away rather than finishing it and publishing it with him

388
00:34:26,600 --> 00:34:32,200
I do regret that very much but in my high school you know we only had one high school

389
00:34:32,200 --> 00:34:37,200
in town in Ithaca oh some of the parents so I didn't know what people's parents did but

390
00:34:37,200 --> 00:34:42,680
some of the parents turned out to be chemists and material scientists and of that we had

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one math class that kept in touch even before social media we ended up with five surface

392
00:34:47,960 --> 00:34:55,520
scientists two Greek scholars two economics professors and one close friend who ran at

393
00:34:55,520 --> 00:35:01,080
that starting her career and Alzheimer's Foundation and now at a different a different foundation

394
00:35:01,080 --> 00:35:07,920
so we had we had a we had a pretty good crew there the guy who sat next to me in high school

395
00:35:07,920 --> 00:35:12,800
runs a 200 million dollar material science institute in Korea now and I chair his advisory

396
00:35:12,800 --> 00:35:13,800
board.

397
00:35:13,800 --> 00:35:14,800
Oh so cool.

398
00:35:14,800 --> 00:35:19,600
Yeah it's very funny they connect after all these years when I went back to visit Cornell

399
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I met the you know met the faculty and I really just knew them as the parents of my of my

400
00:35:26,280 --> 00:35:30,440
you know elementary school through high school friends and some are pretty famous chemists

401
00:35:30,440 --> 00:35:34,160
and material scientists but the first thing when I'd walk in their office I'd go oh

402
00:35:34,160 --> 00:35:40,320
you're Yoni's father oh you're so and so it was a kind of a surreal visit the first

403
00:35:40,320 --> 00:35:43,800
time I did and then later I figured it out you know I figured out who everybody was and

404
00:35:43,800 --> 00:35:49,160
how they were connected academically and and in terms of families.

405
00:35:49,160 --> 00:35:55,360
Yeah they say you are the average of the five people you're around the most and so yeah

406
00:35:55,360 --> 00:36:03,600
like you're around like spectacular human beings and yeah the people rub off on you

407
00:36:03,600 --> 00:36:08,040
it's I tell my dog about all the time like be careful who your friends are.

408
00:36:08,040 --> 00:36:09,040
Perfect.

409
00:36:09,040 --> 00:36:14,520
Well I had my brother my two older brothers who were both both women of science and medicine

410
00:36:14,520 --> 00:36:15,520
before me.

411
00:36:15,520 --> 00:36:19,760
Oh I thought you were I thought you were gonna say one is a drunk the other one is in jail

412
00:36:19,760 --> 00:36:22,720
oh no no they turned out.

413
00:36:22,720 --> 00:36:24,520
Unless you know something I don't know.

414
00:36:24,520 --> 00:36:26,520
No no he turned out good.

415
00:36:26,520 --> 00:36:31,400
Why don't I have a birthday tomorrow I'll check I'll check to make sure he's he's safe.

416
00:36:31,400 --> 00:36:37,360
Nice not one bad pancake man that's so awesome that's awesome.

417
00:36:37,360 --> 00:36:44,920
What are you like most proud of Paul like what is the big hall breakthrough or is it

418
00:36:44,920 --> 00:36:46,600
yet to come.

419
00:36:46,600 --> 00:36:47,600
Well I hope it's yet to come.

420
00:36:47,600 --> 00:36:48,600
Me too.

421
00:36:48,600 --> 00:36:55,920
I'll tell you a couple things we've done along the way so the as I said early in my career

422
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I was trying to figure out how to how electronic structure and chemistry were coupled and we

423
00:37:00,640 --> 00:37:04,560
figured out how to measure that every day and that was kind of a crisis in my career

424
00:37:04,560 --> 00:37:09,560
that it was supposed to take longer and the next thing we did was taking inspiration from

425
00:37:09,560 --> 00:37:17,560
biology and trying to understand how the very efficient motors in the body work so we have

426
00:37:17,560 --> 00:37:24,440
motors in us where the chemical fuel is turned to motion with more than 99 percent efficiency

427
00:37:24,440 --> 00:37:29,840
so much so that they're used to pump protons through a membrane if you push the protons

428
00:37:29,840 --> 00:37:36,080
back the other way you get the fuel back it's almost like you know the the scene in Ferris

429
00:37:36,080 --> 00:37:41,040
Bueller where they're trying to make the miles off the odometer by running it backwards on

430
00:37:41,040 --> 00:37:45,560
blocks it's almost as if they're trying to get the fuel back in the tank there's nothing

431
00:37:45,560 --> 00:37:52,000
that humans can do at any scale that is close to that efficiency and so what we try and

432
00:37:52,000 --> 00:37:58,320
do in in my laboratory is recapitulate natural features where we understand where every atom

433
00:37:58,320 --> 00:38:05,240
is so we go from quantum mechanics to engineering and experiment theory and simulation where

434
00:38:05,240 --> 00:38:10,400
basically we do the experiments then we test different theories and we figured out now

435
00:38:10,400 --> 00:38:17,240
a whole series of these functional molecules by developing both microscopes that can measure

436
00:38:17,240 --> 00:38:21,080
their function and their structure at the same time and tens and hundreds of thousands

437
00:38:21,080 --> 00:38:26,760
of times just for a single molecule or assembly not the same one chemically but the very same

438
00:38:26,760 --> 00:38:33,940
actual one and then along the way we developed this you know this ability to place chemical

439
00:38:33,940 --> 00:38:40,160
functionality that I mentioned before so that became sort of a thing if you will you know

440
00:38:40,160 --> 00:38:45,000
people came up with different functional systems and then asked us to figure out the mechanisms

441
00:38:45,000 --> 00:38:54,640
or more often sort out between opposing ideas about how they worked and then the long came

442
00:38:54,640 --> 00:39:01,480
hand really I was very much curiosity driven and she's focused on anxiety and depression

443
00:39:01,480 --> 00:39:07,600
and how neurotransmission works and that really led me to target problems you know first first

444
00:39:07,600 --> 00:39:14,520
hers a later one in doing safe efficient high throughput gene editing and now institutionalizing

445
00:39:14,520 --> 00:39:18,600
that and that's what we're having our meeting next week about that I hope you're going to

446
00:39:18,600 --> 00:39:23,840
join on this challenge initiative where we identify unsolved problems and then we put

447
00:39:23,840 --> 00:39:29,540
teams together to go after them and develop novel effective solutions for them and that's

448
00:39:29,540 --> 00:39:38,040
become just an exciting way to live where you know you ask someone what would revolutionize

449
00:39:38,040 --> 00:39:43,160
their field if you could do it or what would they like to be able to say they could do

450
00:39:43,160 --> 00:39:48,840
in 10 years or in medicine very specifically you know what did a particular patient need

451
00:39:48,840 --> 00:39:55,160
in terms of a device therapeutic or diagnostic so we train medical students and MDPs in our

452
00:39:55,160 --> 00:40:01,400
lab and part of their job is to in their training when they go and see patients to ask that

453
00:40:01,400 --> 00:40:06,760
question and then to bring back the problems to us sometimes they'll propose solutions

454
00:40:06,760 --> 00:40:11,840
sometimes they'll just throw it open and then one of the big advantages of nanoscience is

455
00:40:11,840 --> 00:40:21,160
because the field developed from chemistry physics materials toxicology medicine engineering

456
00:40:21,160 --> 00:40:26,640
you know all these areas we learn communication skills that as far as we can tell nobody else

457
00:40:26,640 --> 00:40:33,400
did and so we shared problems and approaches and we developed new tools between us and

458
00:40:33,400 --> 00:40:38,920
so we developed those skills in our trainees and we also developed them in the medical

459
00:40:38,920 --> 00:40:43,440
students who when they're doctors and they're speaking to intelligent patients or intelligent

460
00:40:43,440 --> 00:40:49,420
parents of patients you know they can explain a disease and what the issues are but to us

461
00:40:49,420 --> 00:40:55,860
many of us have little or no training in biology and medicine they can explain the fundamentals

462
00:40:55,860 --> 00:41:02,640
and then what the issue is and we get very you know the people who propose the solutions

463
00:41:02,640 --> 00:41:08,800
turn out to come from completely different fields the first person to pose an idea for

464
00:41:08,800 --> 00:41:14,800
the gene editing solution was a mechanical engineer working how and how light couples

465
00:41:14,800 --> 00:41:20,400
into nanostructures when we had a project on chronic pain the person who came up with

466
00:41:20,400 --> 00:41:29,200
the best ideas was the writer and director of kung fu panda and he's amazing and we learned

467
00:41:29,200 --> 00:41:35,760
from him all these all these concepts about perception and how you you know you take your

468
00:41:35,760 --> 00:41:42,080
audience and direct their their brain and what they're taking in in a way that scientists

469
00:41:42,080 --> 00:41:46,960
should really be taught and other you know all educators at least and speakers should

470
00:41:46,960 --> 00:41:53,560
be taught to do when you're engaging an audience yeah there was i think there was a show of

471
00:41:53,560 --> 00:41:58,720
maybe over a decade ago on either discovery channel or national geographic and it was

472
00:41:58,720 --> 00:42:05,440
a measure of different types of intelligence so it was emotional like you like you had

473
00:42:05,440 --> 00:42:09,640
to wear goggles that made the world upside down and you had to make a basketball things

474
00:42:09,640 --> 00:42:15,680
like that and the people were were like there was like an air force pilot there were like

475
00:42:15,680 --> 00:42:20,680
all of these people in that group like heavy heavy hitting scientists and at the end of

476
00:42:20,680 --> 00:42:24,280
the day when they did all of the tests and they summed up the totals of the numbers the

477
00:42:24,280 --> 00:42:33,120
winner was this woman from los angeles who writes horror movies she had like overall

478
00:42:33,120 --> 00:42:39,920
iq it was brilliant yeah those writers are smart yeah yeah they got something going for

479
00:42:39,920 --> 00:42:44,000
it's interesting yeah yeah the actor alan alda you know got a little frustrated when

480
00:42:44,000 --> 00:42:50,080
he when he was the host of the show scientific american frontiers so he developed a program

481
00:42:50,080 --> 00:42:54,880
to teach scientists to engage with different audiences whether it's you know your mother

482
00:42:54,880 --> 00:43:00,720
or a legislator or the public or people from different fields or your field we ran a program

483
00:43:00,720 --> 00:43:06,360
with him here at ucla where we brought in nanoscientists neuroscientists and astrophysicists

484
00:43:06,360 --> 00:43:11,600
because those are the areas that the cavoli foundation supports and they are the ones

485
00:43:11,600 --> 00:43:18,280
championing it for us and it was it was really phenomenal to uh you know use ideas from improvisational

486
00:43:18,280 --> 00:43:24,540
comedy to to riff off one another and see what you know understand what your audience

487
00:43:24,540 --> 00:43:29,720
is thinking and really tell a more personal story about why you're interested in a problem

488
00:43:29,720 --> 00:43:35,080
it brings it brings an audience in much closer than they would otherwise be if you were just

489
00:43:35,080 --> 00:43:39,600
you know showing one uh one slide after another actually going back to ferris bueler i guess

490
00:43:39,600 --> 00:43:45,080
yeah i don't know why that one's stuck in my brain right now but apparently it is i see

491
00:43:45,080 --> 00:43:50,240
that it's almost like you watched it yesterday i think mentally we were going to talk earlier

492
00:43:50,240 --> 00:43:57,240
in the week and now that that analogy keeps sticking uh how do you how do you keep up

493
00:43:57,240 --> 00:44:02,760
paul how do you keep up because you know i go to the swiss plasma institute and i mention

494
00:44:02,760 --> 00:44:07,520
your name and they're like oh yeah we know paul and we we're in barcelona and where we

495
00:44:07,520 --> 00:44:12,000
talk to random people and we're like oh i'm here with paul and they're like i know paul

496
00:44:12,000 --> 00:44:17,720
and then i'm in la and i remember meeting constantine right after i might do i think

497
00:44:17,720 --> 00:44:22,800
about two or almost three years ago and it's like yeah i know paul and so there are people

498
00:44:22,800 --> 00:44:28,240
that just know paul there are people at oakridge national labs are like oh paul yeah we love

499
00:44:28,240 --> 00:44:35,040
paul they don't just know you they love you they love you and they see you as a person

500
00:44:35,040 --> 00:44:41,560
who remembers the important things about them is like great to collaborate with highly intelligent

501
00:44:41,560 --> 00:44:48,400
and so how do you make time to turn to me to a piece to have so many people think of

502
00:44:48,400 --> 00:44:54,640
you as so exceptional and and how do you do it i have no idea okay great i thought there

503
00:44:54,640 --> 00:44:59,640
would be my my life is chaos i think you know i'm very sort of the key to my happiness and

504
00:44:59,640 --> 00:45:04,080
i'm very forgetful in a way i think whatever's in front of me is what i'm working on and

505
00:45:04,080 --> 00:45:09,720
so we can have these divergent you know pieces in our group and you know i can work with

506
00:45:09,720 --> 00:45:15,760
cronos your company our company and then go work on growing meat and fish in the laboratory

507
00:45:15,760 --> 00:45:23,720
and then go back to trying to figure out how you know charge moves through biomolecules

508
00:45:23,720 --> 00:45:29,040
and how we might take advantage of that it's just kind of whatever i keep a list of things

509
00:45:29,040 --> 00:45:35,840
i'm supposed to do and then when something pops to the top i'll just focus on that completely

510
00:45:35,840 --> 00:45:42,760
i think i'm curious about the you know the world and the connections in it and there

511
00:45:42,760 --> 00:45:49,040
are some really part of that's the nano you know the whole nano view that that i talked

512
00:45:49,040 --> 00:45:53,880
about where it's much bigger than nano we're responsible for doing more because we have

513
00:45:53,880 --> 00:45:59,520
developed these special skills and and maybe i've maybe i've exploited that more than most

514
00:45:59,520 --> 00:46:04,000
people i've started and ran this journal that let me see whatever in the world was doing

515
00:46:04,000 --> 00:46:10,080
i towards the end the rate of submissions was i think 12 000 manuscripts a year and

516
00:46:10,080 --> 00:46:15,120
i'd read and rate every one of them and so by knowing what people were doing it really

517
00:46:15,120 --> 00:46:21,360
helped to see how everything fit and could fit together and so i'm still after that but

518
00:46:21,360 --> 00:46:26,280
now in different ways you know part of that is cronos and some of the other startups that

519
00:46:26,280 --> 00:46:32,560
seems to use the same part of the brain that i used as an editor yeah that's that's so

520
00:46:32,560 --> 00:46:37,720
that's awesome it's almost as if like you do the things that make you happy and that

521
00:46:37,720 --> 00:46:43,000
have like great purpose in the world and and doing well well that's my advice to young

522
00:46:43,000 --> 00:46:47,280
scientists they that they should figure out what makes them want to get out of bed in

523
00:46:47,280 --> 00:46:52,040
the morning and get in the lab and you know for your i did something completely different

524
00:46:52,040 --> 00:46:58,320
for my phd you're not stuck with that forever but a phd i just finished teaching the you

525
00:46:58,320 --> 00:47:04,280
know top 40 freshmen at ucla in science and engineering and got them all on laboratories

526
00:47:04,280 --> 00:47:08,560
and you know you can change your mind later you can learn from what you did what you like

527
00:47:08,560 --> 00:47:14,320
and what you don't like and that's also that's also important but if you're going to go spend

528
00:47:14,320 --> 00:47:20,040
five years on something for her phd it ought to be something you really enjoy because that's

529
00:47:20,040 --> 00:47:24,280
a big chunk your life it shouldn't just be oh this is important to work with this famous

530
00:47:24,280 --> 00:47:29,200
scientist or that you know at that you know institution that's ranked higher than another

531
00:47:29,200 --> 00:47:35,920
one find what's just so exciting you can't wait to do it again i think one of the beautiful

532
00:47:35,920 --> 00:47:41,640
parts of academics is we get to do that every day and we can we can morph and change or

533
00:47:41,640 --> 00:47:48,160
add things or stop doing things we have the freedom to to explore there and then in my

534
00:47:48,160 --> 00:47:54,160
group especially we use everybody's brains you know we're much smarter as a team than

535
00:47:54,160 --> 00:48:00,160
than any one of us by far and so we're always batting around ideas and we ask everybody

536
00:48:00,160 --> 00:48:05,600
in the lab from high school students through visiting professors to pitch ideas and defend

537
00:48:05,600 --> 00:48:10,680
them and say here's what would matter you know here's why this would matter here's what

538
00:48:10,680 --> 00:48:15,400
we need to do it here the resources we need here's what we need to stop doing and that's

539
00:48:15,400 --> 00:48:19,960
just the philosophy of how the group of how the group works and everyone is expected to

540
00:48:19,960 --> 00:48:25,560
do that later if someone's going into academics that's the way you get a job as you say this

541
00:48:25,560 --> 00:48:29,160
is the most important problem in the world and i'm the only one who can tackle it or

542
00:48:29,160 --> 00:48:34,440
if you're in a company then you say here's what we ought to be doing and here are the

543
00:48:34,440 --> 00:48:40,840
sources i'll need to lead it and then that's how you end up having a group and running

544
00:48:40,840 --> 00:48:45,600
it we have people who've become lawyers out of the group and they use those same gun arguments

545
00:48:45,600 --> 00:48:52,400
in their firms yeah you gotta you gotta love what you do i i'm i'm very grateful for that

546
00:48:52,400 --> 00:48:59,000
with chronos i've had hundreds of jobs but and some i've loved others i've not loved

547
00:48:59,000 --> 00:49:05,080
and the ones that i've always done well are the ones i loved so it's obvious in in working

548
00:49:05,080 --> 00:49:11,040
with you i'm like how important this is to you and how much joy it brings you and us

549
00:49:11,040 --> 00:49:17,200
it's fun to work together and one of the one of my great great pleasures is learning about

550
00:49:17,200 --> 00:49:22,520
magnet problems from carl and seeing what we might be able to do that's novel there

551
00:49:22,520 --> 00:49:28,040
that'll that'll move the whole field forward right yeah i'm definitely excited about that

552
00:49:28,040 --> 00:49:33,760
we could we could close off with the chronos question and yeah what what how do you feel

553
00:49:33,760 --> 00:49:41,120
about fusion energy hall have i made you more gung-ho about it in these last three years

554
00:49:41,120 --> 00:49:48,960
uh you know we i i think you are the third person to ever join our company so you've

555
00:49:48,960 --> 00:49:55,240
been with us from from very very early days and you've been with us since i think of even

556
00:49:55,240 --> 00:50:03,640
like two months before carl joined us so amazing yeah yeah i think that's been that's really

557
00:50:03,640 --> 00:50:10,000
it's been extraordinary to to identify what the key challenges are and then of course

558
00:50:10,000 --> 00:50:13,980
we're not operating in a vacuum if you're part of an expression seeing how the other

559
00:50:13,980 --> 00:50:18,800
startups in the area are approaching the the problems and where they've tripped up and

560
00:50:18,800 --> 00:50:24,520
where they've moved forward and so it's a it's a very exciting time to say okay we could

561
00:50:24,520 --> 00:50:30,800
solve this problem this problem this problem in fact there are a lot of analogies to some

562
00:50:30,800 --> 00:50:37,040
of the other big programs that we've undertaken as humans you know in the human genome project

563
00:50:37,040 --> 00:50:43,880
there were a couple of leaps that were identified and then a quite small number of people figured

564
00:50:43,880 --> 00:50:50,040
out the solution to those and i'm seeing echoes of that in what we're doing as well and so

565
00:50:50,040 --> 00:50:55,260
i i see great promise and and particularly the team we have and how we're approaching

566
00:50:55,260 --> 00:51:03,000
those problems so i'm very excited about it