Steven Wilson: Craving Chemical Efficiency

SPEAKERS

Sarah Webb, Steven Wilson

Sarah Webb  00:03

This is Science in Parallel, and I’m your host, Sarah Webb, in this episode, I’m speaking with Stephen Wilson of Arizona State University. Steven completed his Ph.D. there in 2024 and has continued to work as a postdoc in chemical engineering. He and I discussed how his research evolved from experimental to computational work, how his 10-year experience in the Navy shaped his career, and his advice for the marathon of completing a Ph.D. Steven’s graduate research was supported by a Department of Energy Computational Science Graduate Fellowship from 2021 to 2024, which we refer to as CSGF for short. This podcast is a media outreach project of that program. Steven and I spoke in July at the CSGF program review in Washington, D.C., an annual gathering for fellows to network and present their research.

Sarah Webb  01:05

Steven, it is great to have you on the podcast.

Steven Wilson  01:14

Thank you, Sarah. I’m very glad to be here.

Sarah Webb  01:16

So first of all, for our listeners, let’s just start with a really kind of high level elevator pitch of what it is that you are currently working on.

Steven Wilson  01:27

I’m a postdoctoral fellow at Arizona State University and chemical engineering and in the Muhich lab group. So I’m a computational chemist by trade. So I do, as it sort of sounds, chemistry on the computer, and I look at chemical reactions and various different chemical properties using computational means. So my current work, my post doc work is associated with plasma interactions and reducing iron ore with plasma and looking at if we can make that a more carbon neutral and energy efficient process.

Sarah Webb  01:55

Great. So I want to kind of take a step back and learn how you got into doing all of this? Where did your early interests in science and engineering come from?

Steven Wilson  02:08

I did have an early interest in science. I was usually, generally pretty good in math and science. Growing up really early on, my dad got me interested in science with this rocky you make your own rock crystals kit type of thing. And so from from then on, I was really interested in science in all aspects. I actually fell a little more in love with astronomy a little later on, when I went to natural observable laboratory. I saw Saturn for the first time. And so then the scientific field in general sort of stemmed from there, and then my path sort of grew from different aspects into where it is now,

Sarah Webb  02:38

Great. I know before you got into research, you spent time in the military. How did that happen? Tell me a little bit about kind of your career and where it started.

Steven Wilson  02:49

Yeah, I enlisted in the Navy right out of high school, and that’s sort of where my career really started. I joined the Navy Nuclear Program, which is the longest enlisted school you can go to, and one of the most difficult. You sort of learn nuclear physics and thermodynamics and everything in a really short period of time. In short period of time, meaning about a year and a half, roughly. And then I had some extra training to become what’s called an engineering laboratory technician, which basically just means I did the chemistry for the reactor and radiological controls. And so in all, it was about two years of school. And then from there, I go out and serve in the fleet. I was an aircraft carrier and out of San Diego, and then various positions from there, over a 10-year span.

Sarah Webb 03:25

So all over the world, I imagine,

Steven Wilson  03:27

 Yeah. So station wise, I was in San Diego, Washington State and South Carolina, but where I visited was, I was what’s called a Westpac, so it’s the Pacific or Eurasia ports: Singapore, Thailand, Hong Kong, Dubai, Bahrain, lots, lots of places,

Sarah Webb  03:43

Wow. So what do you think were kind of the most important things that you learned, either scientifically or personally, from that experience?

Steven Wilson  03:53

The Navy experience really allowed me to grow. I was, you know, maybe, as most people that are good with math and science, little awkward, a little not socially adept. So the military experience in general, plunges you into that kind of world. But scientifically, because I was still working with nuclear power, I got to really keep my interest in the science, not an academic fashion, right? So I was still learning a lot of new things every day, and then growing all these really, what some people call soft skills, time management stuff, but in a military sense. It’s very accelerated, very fine tuned. You know, there are strict time requirements. There’s a strict uniform. Everything is strict. So you really develop this strict, rigid character about yourself.

Sarah Webb  04:31

Well, thank you for your service.

Steven Wilson  04:33

Thank you. Yeah.

Sarah Webb  04:34

And so you finished up your time in the Navy. What led you to chemical engineering?

Steven Wilson  04:41

Chemical engineering sort of aligned with what I was doing in the Navy. So again, as an engineering laboratory technician for the reactors, I was maintaining chemistry and radiological controls. That was my basic training. But as you know, in a 10-year work career, essentially, I worked up the ranks, and I was actually at much higher levels of engineering operations. Operations, maintenance and everything in the equipment, achieving, eventually what’s called engineering officer of the watch, so you oversee everything in the reactor plant and mechanical plant operations. And so from an engineering aspect, it’s large plant system and all these different moving parts. And so I knew from there I wanted to do engineering, and then I knew I wanted to do chemical engineering. For my background in chemistry, and it sort of married the two perfectly.

Sarah Webb  05:21

Okay, so chemical engineering, did you know that you wanted to do research, or was that something that came along the way?

Steven Wilson  05:28

I did not know I wanted to do research, so not in my first year. I knew it’s a hard transition going from military to academia, especially I think I was 28. I think I was 28 at the time starting undergrad, so a very different path, and I was just learning the first year, just that transition and going into into school. But it only took about a year before I realized I sort of wanted something more than just the run of the mill, take my classes, do my homework. I wanted to see what else was involved. That’s when I started looking for research opportunities.

Sarah Webb  05:56

What was your first research opportunity?

Steven Wilson  05:58

 The first one stuck. I guess you could say. I played around with the idea a bit, but the first he talked to a couple professors at Arizona State. So I did my undergrad at Arizona State and my Ph.D., and that’s because I met my PI the same year he actually started at Arizona. So Chris Muhich was just joining in 2018. He was looking for undergrads to sort of start facilitating his lab and other aspects of it. And so we hit it off right away, I think.

Sarah Webb  06:23

Did you start working on then, sort of the fundamentals of what you’re working on now? Or has that evolved over time?

Steven Wilson  06:30

It’s definitely evolved over time. So in Chris’ group, I actually started in the lab, so not a computational at all, really, because he was starting to grow his lab. And with my background of already 10 years and knowing how to work certain equipment and construct lab equipment, in a sense, I helped him build some of his actual experimental lab work until he got his first Ph.D. student. And then some other people that came on to do the experimental side of things. And that’s when I transitioned to computational because that also did interest me. And that’s where I’ve sort of been, not now, I don’t want to say stuck, but that’s where I’ve been. That’s where I’ve been. It’s where I’ve been since, and I’ve enjoyed it since.

Sarah Webb  07:06

So talk about the computing part. Where did computing come in for you? And was that something that was an interest all along? I’m sure you were dealing with computers when you were in the military as well.

Steven Wilson  07:20

Yeah, the way I use computers now is very different than I did in the military, and very different. So I grew up, you know? What is it? Millennial, I guess is the term, right? And I grew up without computers until maybe I was in my early teens, I guess is when I first started interacting with computers. And, you know, I was actually asking some people in my lab group recently, you know, do they still have, do they still require, like, Microsoft Office as a class in high school? Because it was, for me, it was, it was one, right? And they said, No, they don’t. It’s not a requirement. But in any case, this is the first time, when I started at ASU and Chris’ group, that I really understood what high-performance computing was. I was very unaware of what that meant in the beginning, and then I learned how we can apply it, and we sort of marry this idea in computational chemistry. Anyways, physics and chemistry, which are both against very deep scientific fields and interests me a lot. And so it still piques my interest every day, as I get to look at now not just one chemical system or one chemical compound for a nonexperimental side, and become an expert in that, but I can become an expert in a lot of different chemistry. It doesn’t have to just be one specific type. And so that’s the exciting part of computational chemistry.

Sarah Webb  08:16

So you’re talking about this evolution, though, from being an experimental researcher to a computational researcher. What led you into that trajectory?

Sarah Webb  08:27

Yeah? So that, yeah, it sort of it evolved with my PI’s lab. So he brought on a Ph.D. student that sort of took over the experimental side of things, and now he had the gap on the computational side. And so I’ve sort of been that person, because I’ve been with him for so actually, I’ve been with him the longest, and in the group of everybody that’s in the group. And so I’ve always been the person to sort of fill that role of something that he needs along the way, because I’ve always been interested in something new. I’ll learn something a lot, drive it as far as I can, and then not exactly switch gears, but apply everything that I just learned from that one specific system to this next gap that he wants to fill. And that’s sort of how it evolved into computational chemistry from experimental and then in the experimental and then in the computational chemistry side from one topic to the next.

Sarah Webb  09:13

So you’re the utility player who pushes the envelope?

Steven Wilson  09:13

A little bit. I’ve grown a lot since so there’s a lot more utility players out there, but, yes, I’ve done a lot of that.

Sarah Webb  09:17

Or it sounds like maybe that’s partially your interest and skill set maybe.

Steven Wilson  09:20

Yes, I always Yes. I’m curious about everything which can be, which can be a bad thing as as well, when you can’t focus on just one topic. But I do like to look at everything that I possibly can.

Sarah Webb  09:31

That sounds really great, though. So let’s talk about a couple research projects. I guess maybe let’s talk about your Ph.D. research first, and kind of, you know, give me a sense of kind of what you focused on during that time.

Steven Wilson  09:47

Yeah, so in a broad sense, applying computational chemistry, I looked at, how do we more efficiently find materials for specific chemical reaction: in this case, something called chemical looping, where you take a material and in a cyclic fashion, so you can recover it drive a reaction that gives you products. In this case, we wanted to form hydrogen fuel from essentially steam and sunlight, and then you can use that hydrogen fuel for either storage or  a lot of different things for hydrogen you can use for hydrogen. But then you can keep doing that reaction over and over and over again. And so my goal was to build a high-throughput process that can more efficiently find or optimize materials for that chemical reaction. And there’s a lot of different a lot of different avenues that I did trying to find that, but that’s the overall

Sarah Webb  10:33

So what were the challenges? Or maybe pick a particular challenge that you had to overcome, or how did you sort of work through the process of finding answers to your questions?

Steven Wilson  10:46

Yeah. So the overall challenge was that at every part along the process of finding these materials was a human interface which can slow things down. It becomes sort of an Edisonian approach to I need to try this material, change one thing, try it again, change one thing, try it again. And that’s not conducive to high throughput and trying to span thousands of materials that can take years. And so one of my one of how I look at things is I like to make things more efficient. It’s really troublesome to me when something, I look at something and I can clearly see some inefficiencies somewhere.

Steven Wilson  11:15

And so I’m terrible in traffic, I’m terrible standing in lines, because I can always see, or at least what I think is a more efficient way of doing something. And so it really bothers me. And so the first thing I looked at was the way we model these thermodynamics for these materials. And the thermodynamics are sort of what govern how well it will perform in the given chemical reaction. And so the very human approach was that you have to design a very specific model for the very specific system. So one chemical compound that you’re looking at, and then you have to do that. You have to change that model for every chemical compound. And so I wanted to see if there was a different way where you can take a more broad model and apply it to several different chemical compounds, so you can get their thermodynamics over 1000s of materials and compare them more efficiently, more quickly. And that was the first, very first one.

Sarah Webb  12:02

What’s the status of that work? Are you still working on it?

Steven Wilson  12:06

So yes, right now, I still work on it a little bit, but someone in the group has started, actually, at the beginning of this year. It’s sort of taking over my role as I sort of transition into refining that, what we call the CrossFit CEF. CEF stands for compound energy formalism, and they’re sort of trying to adapt it to be even more efficient than I made it, because everything always become more efficient. And so right now, we’ve done a lot of work in showing its feasibility. There’s, I think, three, at least three articles out there right now on it, and sort of just waiting for that field adoption, sort of still early in its phase.

Sarah Webb  12:37

What would you say are the barriers to adoption at this point?

Steven Wilson  12:40

it’s relatively new, and it does require an adoption of the way you collect your data. This is the way the experimentalists, in their sense, collect their data, is right now the process that they use is collect a lot of data points in a very fine grid, so very close together, points that they’re sampling, and then they build that model that they used to build by hand. And it’s a very iterative process, and they’re very comfortable with it, right? It’s been maybe 20, 30 years that they’ve been applying this process to these materials. And so since it’s brand new, and it requires a shift in gathering data points that are actually far apart to fit my type of model. It can be a slow adoption, as with any brand new process.

Sarah Webb  13:18

Tell me more about what you’re working on now.

Steven Wilson  13:20

Yeah, so from there again, still efficiency wise, building on that idea of how can we more quickly sample materials, and especially in a computational sense, we looked at using Bayesian statistics to inform people how they sample experimental data points. So a priori, you can determine what is the next data point you should sample without needing to know anything else about the system. And then from there, in my postdoc, we’ve started from the expediting the computational side of things with a company, startup with me and my PI called PsaiForge, psai being P-S-A-I, because we’re including artificial intelligence in this process. But what PsaiForge is is sort of a wrapper around current quantum chemistry software that speeds it up and makes it faster to come to an answer. And so that way, PsaiForge will do all this mundane work while you can focus on the science.

Sarah Webb  14:10

Nice. In terms of the systems that you can apply these tools to. I mean, are there atomic limits? Are there chemical limits? I mean how generally applicable?

Steven Wilson  14:21

As with a lot of way, the way computational things work, The simpler you make the problem, the bigger you can apply the problem to. So there’s what are called, either different functionals or different processes in which you can apply to thousands of atoms or hundreds of thousands of atoms. But if you want to get down to chemical accuracy, you have to change a functional and then you’re looking at maybe hundreds to 200 atoms at most. And there’s a lot of different reasons for that. One is just that the way quantum chemistry works. It scales horribly with the atom size, specifically the number of electrons. And that’s what PsaiForge tries to address is getting around that scaling problem by expediting the computational side of things with AI. And then we don’t have to worry too much about the size of the system, and then just more. So now, can we get AI to get to chemical accuracy, no matter the size of the system.

Sarah Webb  14:21

 What do you think is next for you?

Steven Wilson  14:32

So I’ve been a postdoc for about a year now, in that first year, so right after I finished my Ph.D., my PI and I decided we’re going to try, I was going to try, I was going to try to apply to academic positions. The academic and postdoc world has been a little hit heavy right now, and it’s really scarce opportunities right now. And so in the meantime, this startup started, one, because this project is sort of something my PIs thought about, we thought about this for a long time, and we’ve been working on it here and there for a long time. And we finally decided, you know, this is an avenue that we can pursue while we figure out what where, while I’m still applying to things. And so we’re applying for funding right now. And I mean, if it gets funded, this can definitely be my full-time thing as far as work is concerned, but I would still, I’m still looking at other postdocs and academic avenues.

Sarah Webb  15:57

Great. Good luck.

Steven Wilson  15:58

Thank you.

Sarah Webb  15:59

So a slightly broader note. I mean, obviously part of why we’re talking is because we’re here at the CSGF Program Review. And I want to learn from you a little bit about your CSGF experience and what you’ve learned, how that shaped your career so far.

Steven Wilson  16:14

Yeah, getting awarded the CSGF is probably one of the best things, I think definitely the best things that happened to me during my Ph.D. It allows so much flexibility in what you can research. I wouldn’t have researched any of the stuff that I did without the CSGF. You know,  you bring your own money to the to the group, and so you can research whatever you want. And so there’s a lot of flexibility. And then on top of just that, just that flexibility now, now you get to interact with a very unique cohort of people from a very broad range of sciences. And so you have access to just a lot of good resources between the cohort and other fellows as well as alumni.

Steven Wilson  16:37

The alumni are indispensable. They provide so much information. Just go on to see the CSGF Slack channel, and you’ll see all kinds of people asking questions every day, so from the personnel side of things. But then the resources as far as you get to go do a practicum at a national lab, and you can do as many as you want, I think. You must, must do one, but as many as you want, that you can fit into your Ph.D., and you get to experience something outside of your current field, which broadening that science. And again, as I mentioned earlier, all sciences interest me, so getting to look at something different for a little bit, always just excites me.

Sarah Webb  17:23

Well, tell me a little bit about your practicum then.

Steven Wilson  17:26

Yeah, I did my practicum at PNNL. I looked at, so it was still computational side of things, but I did instead of what I currently do, which is called density functional theory, DFT, for short, I did molecular dynamic studies instead. So again, using that different functional maybe you can say, to look at water droplets of serine, and we were trying to get thermodynamic properties from computational calculations. That was under Sam Johnson. She’s actually moved away since PNNL. She’s at Colorado School of Mines now as a faculty, which is also exciting for her. So again, your thing with the fellowship is, you know, you just create this network of people that travel all over the place, and just open more doors everywhere so.

Sarah Webb  18:05

And that’s a lovely segue, because I’m interested in the people who you feel like have influenced your career, either people who have been active mentors, people have been role models for you in terms of things that you’ve thought about in your scientific career so far.

Steven Wilson  18:19

I’ve had a lot of mentors along the way, and I look at mentors, I’ve had both good and bad mentors, or leaders, you can say. And so you take all the good things and try and leave the bad things behind from every leader that you come across, and that was throughout the military and now into my academic career. And so my biggest academic mentor has been my PI Chris Muhich at Arizona State. He’s definitely shaped my path through academia. He’s been helpful. I mean, he helped me write the CSGF app package, right? So a lot I can almost everything I can attribute to my success in academia has been through him. For sure.

Sarah Webb  18:54

What do you think has been, you know, is there either a particular story or a particular quality about him that you think has been particularly important for you and your development?

Steven Wilson  19:03

Well, I mean, I’m a very unique, a very unique background being pretty much the same age as my PI. I think he’s maybe six months older than me, so or six or eight months. It’s not, I can’t remember exactly, so it was very comforting. He didn’t. I wasn’t just like a student to him. I wasn’t. It was very much as a close relationship, almost friends. You know, you have that barrier between PI and student, but he didn’t just, he didn’t just treat you as me, as a student. That would have felt very weird to me being the same age, and I always already felt weird in classes, being about 10 years older than everybody. So I didn’t have those regular group study sessions till 12 am with people. I usually had my hard cutoff just being, you know, eight, yeah, but you get a bedtime as you get older, right? Exactly. So it was comforting in his group to sort of have that I could still have that academic relationship and not feel like I was in a weird spot. He made it very comfortable.

Sarah Webb  19:57

That’s awesome. That’s awesome. I mean, it sounds like there. And elements of feeling a little bit like a fish out of water at times. But I did a Ph.D., too, and one of the things that I think, that I very much appreciated in my own academic experience were people who had a few years away, a different set of life experiences, and the way in which those people thought about problems differently, and how they brought their career experience, life experience, to their science. And I wondered what you felt like your added life experience and the experience in the Navy, what do you think that that brings to the table for you?

Steven Wilson  20:44

 My specific experience through the Navy really gave me a lot of hands on experience from the things that I learned in my undergrad, for sure, especially in chemical engineering. And so when I got to the point, you know, in a common chemical engineering undergrad, there’s a junior lab, in a senior lab, where you’re supposed to gain that hands on experience. And these were all already familiar things to me. And so the operation side of things was already secondhand knowledge to me. It was very instinctual. And I just had to make sure I married the deep theoretical side of things that I was learning to that operational side of things. And so I got the it’s what I consider is sort of this instinctual understanding that I think a lot of what colleges try to teach can be hard without experience. So, and that’s what, that’s where you get that when you have a real world experience, and then later come back and you just have to tie in theoretical. I think it’s easier to tie in theoretical once you have the real world experience, versus the other way, at least. That’s what I felt going through,

Sarah Webb  21:41

And I guess as a graduate student, because a Ph.D. is different from the undergrad part of it. Is there anything that you think that that brought you as you had to think about this, you know, running your own research project and dealing with the problems and roadblocks that come up along the way?

Steven Wilson  21:58

Definitely. so on that side of things. So the Ph.D., that Ph.D. is different, right? So the point of the Ph.D. is, you’re looking at something fundamentally different and never done before, making the, what we call the little notch in the circle of knowledge. That’s that’s just hasn’t been studied before. And so in that case, what I relied on from my past experience was just the structure that I learned along the way. And by structure, I mean, you know, structure of your day: time management on certain projects, which I notice is hard in meeting the deadlines and pushing that project along and knowing where and when you need to stop pushing on one thing because it’s reached a certain acceptable limit. And going to the next thing and sort of putting all the pieces together before you refine it. And so it’s just building that structure. I did have a hard. I’d say part of my Ph.D. was during COVID, and so I did have a hard time working from home. I’m not a big work-from-home person, because, again, part of that structure for me is going into work. I have, I have a really hard shift in mentality from at home to working at work and then going back home. It’s, it was just inherent, inherent in me. And so I think that’s definitely what helped me in a successful Ph.D., is all that structure that I already had in place.

Sarah Webb  21:58

Is there anything that we haven’t talked about that you think is important to mention about your science, about your career path?

Steven Wilson  22:33

Just in general, I think I like the idea that the flexibility and everything like the CSGF allows and the Ph.D. in general is that it sort of anyone can achieve their Ph.D. And so, like, I I’m not sure if I mentioned it earlier yet or not, but I was growing a family outside of the Ph.D., and so it’s possible for anybody to do. And so anyone that listens and thinks they’re sort of lost in this Ph.D. world, because it can be very drowning sometimes with all these deadlines and stuff, just know there is always light at the end of the tunnel, and you can manage it. And you can definitely achieve whatever you need to achieve.

Sarah Webb  22:55

If you wanted to pass along a nugget of advice for an early career researcher or someone behind you, or maybe somebody who’s coming in as a nontraditional student, what words of wisdom would you pass along?

Steven Wilson  22:55

My advice is definitely build that structure in your life because the routine really helps keep things in line. So you always know you can always expect what’s next, because there’s can be there’s a lot of work associated with the Ph.D. And so if you can just set that routine of doing the same thing ever the simple things every day, then all the hard things sort of fall into place as well. And so you build that structure in your life, and what I sort of associate it with is, I forget the reference, but there’s, it takes 10,000 hours to master a task. And if you break it down for 10,000 hours, if you do nine hour days, it takes about five years to master whatever you’re looking at. And that’s about the same timeline as a Ph.D., which I thought was an interesting coincidence. So if you just structure out that nine hours, five days a week and work towards and realize it’s a long-term thing, and not like you’re always thinking about the next deadline, which seems so short, and then you never realize that this is playing the long game, and getting getting to the Ph.D. takes five years. And so take a deep breath, build that structure and timeline in your day, and you’ll definitely succeed.

Sarah Webb  25:00

That sounds like a wonderful place to end, Steven. Thank you so much. It’s been such a pleasure.

Steven Wilson  25:07

Thank you so much for having me.

Sarah Webb  25:11

To learn more about Steven Wilson’s research and the quantum chemistry startup PsaiForge. Please check out our show notes at scienceinparallel.org. Science in Parallel is produced by the Krell Institute and is a media project of the Department of Energy Computational Science Graduate Fellowship program. Any opinions expressed are those of the speaker and not those of their employers, the Krell Institute or the U.S. Department of Energy. Our music is by Steve O’Reilly. This episode was written and produced by Sarah Webb and edited by Susan Valot.