Molecular Systems Design & Engineering Blog

Dr Ed Pyzer-Knapp is Head of Research Innovation for IBM’s UK and Ireland labs.  He is interested how emerging technologies, such as AI and quantum computing, can help to answer some of the biggest challenges of our time, with a particular focus on novel molecular discovery and optimization. He obtained his PhD from the University of Cambridge, and then moved to Harvard University, leaving to help start the IBM Research Lab in the UK in 2015. He holds a visiting Professorship at the University of Liverpool, is the Editor in Chief of Applied AI Letters, and the Co-Chair of the UK Young Academy – the UK’s national academy for early to mid career professionals.

Read Edward’s Emerging Investigator manuscript: A physics-inspired approach to the understanding of molecular representations and models, DOI:10.1039/D3ME00189J

This is an Open Access article and free to read!

Follow Edward on Linkedin.

Yongchul G. Chung is currently associate Professor in the School of Chemical Engineering at Pusan National University, Busan, South Korea. Prior to his appointment, he received PhD in Chemical Engineering from Case Western Reserve University in 2013, working on polymer glass simulation. In 2013, he joined Randall Q. Snurr’s group at Northwestern University as a postdoctoral fellow to work on the projects related to Nanoporous Materials Genome Center. His current research interests are carbon dioxide capture and utilization, applying machine learning techniques to chemical engineering and chemistry problems, and molecular modeling of nanoporous materials.

Read Yongchul’s Emerging Investigator manuscript: Recent advances in software tools for adsorption science and engineering, DOI:10.1039/D2ME00036A

This will be free to read until May 30th! Read the interview with Yongchul below;

1. How do you feel about MSDE as a place to publish research on this topic?

I find articles published in MSDE to be useful and high-impact. I frequently refer articles from MSDE to members of my group. I think MSDE is a great place to publish multiscale modeling studies.

2. What aspect of your work are you most excited about at the moment and what do you find most challenging about your research?

I am excited about the possibility of multiscale modeling in the future, where the information from molecular simulation is used in device or unit operation levels to truly evaluate the performance of materials and accelerate the overall materials discovery process. The current challenge is availability of open-source software tools for modeling at the systems level. Majority of the research is done using commercial softwares and even if the software has been developed, it is not widely adopted in the community to share their code. I hope this will change in the future to truly accelerate scientific and engineering progress.

3. In your opinion, what are the most important questions to be asked/answered in this field of research?

I think the most important question right now is what can we do about carbon dioxide. There are many technologies being reported in the literature, but when will we see widespread adoption (not just a test site) soon to really make the difference? Also we should think about energy transition. If we are going to use energy from renewable sources, what should we do with current infrastructure for oil/gas? Where will we get carbon from to make consumer goods? Many people are working in these areas and I hope to see answers to these questions in the next decade.

Follow Yongchul on X (formally Twitter); @DrYGChung

Dr. Wenjie Xia is currently an Associate Professor in the Department of Aerospace Engineering at Iowa State University. He received his PhD in Civil and Environmental Engineering from Northwestern University in 2016. His research interests lie in modeling and computational design of polymers, soft matter, and multifunctional behaviors of complex materials. He has authored over 80 peer-reviewed papers published in prestigious journals in polymer science, multiscale modeling, and computational materials, including Advanced Materials, Science Advances, Matter, ACS Nano, Nano Letters, etc. He has been recognized by multiple awards, including NSF CAREER Award, ACS PMSE Young Investigator Award, ND-ACES Early Career Faculty Award, RSC MSDE Emerging Investigator, and NIST MML Accolade for Technical Excellence “for leading the development of a new multiscale modeling approach for glass-forming polymer materials”.

Read Wenjie’s Emerging Investigator manuscript: Molecular insights into the hydration of zwitterionic polymers, DOI:10.1039/D3ME00020F

This will be free to read until September 20th! Read the interview with Wenjie below;

1. How do you feel about MSDE as a place to publish research on this topic?

MSDE presents an ideal platform to showcase our group research on modeling and computational design of molecules and polymers. With its reputation for upholding rigorous peer-review standards, MSDE provides a credible avenue to disseminate our research and contribute to the advancements in our field.

2. What aspect of your work are you most excited about at the moment and what do you find most challenging about your research?

I am thrilled by the potential that emerging and advanced computational techniques, such as multiscale modeling and AI, hold for predicting and designing complex molecular systems at a fundamental level. The most challenging aspect of my research lies in establishing an effective strategy to overcome the spatiotemporal limitations of molecular modeling to achieve improved predictions at extended and multiple scales in a computationally efficient manner.

3. In your opinion, what are the most important questions to be asked/answered in this field of research?

In design and engineering of complex molecular systems, such as zwitterionic polymers, the most important question to be answered is how to delineate their “structure-processing-property” relationships of those materials, which requires a fundamental understanding of their intricate intermolecular interactions and microstructures.

4. Can you share one piece of career-related advice or wisdom with other early career scientists?

From my personal experience, it is really valuable to cultivate a strong network of peers, mentors, and collaborators. Building meaningful connections within your field and even beyond really offers diverse perspectives, valuable guidance and tremendous opportunities for research collaboration.

Follow Wenjie on Twitter; @WenJXia

You can also find Wenjie on Linkedin: https://www.linkedin.com/in/wenjie-xia/

Dr. Mehraeen is an Assistant Professor in the Department of Chemical Engineering at the University of Illinois at Chicago (UIC). He received his B.S. from University of Tehran, and M.S. and Ph.D. in Mechanical Engineering from Stanford University. He joined UIC in 2016 after postdoctoral work in the Department of Chemistry at Georgia Institute of Technology and MIT. Professor Mehraeen’s specialty is charge transport in organic photovoltaic (OPV) solar cells, and directed self-assembly of sub-10 nm particles on patterned surfaces. The emphasis of OPVs research thrust is on understanding of the charge transport mechanisms, and the fundamental relationships between morphology of the active materials and bimolecular recombination losses. The focus of directed self-assembly of sub-10 nm particles research thrust is on gaining control of the self-assembly process to make superlattices using flow field and substrate patterning. A new focus of his research activities is on molecular design using a combination of density functional theory, machine learning algorithms, and artificial intelligence. Overall, the objectives of his program are pursued through a combination of theoretical methods and computational simulations, enabling the attainment of a deeper understanding of the core issues of interest to better guide experimental efforts in novel materials synthesis and nanofabrication. Professor Mehraeen’s research work has been funded by NIH, and Strategic Environmental Research and Development Program. Shafigh is the recipient of MIT Ascher Shapiro Award for excellence in educational prowess in 2014, and UIC College of Engineering Teaching Award in 2018, 2019, and 2023.

Read Shafigh’s Emerging Investigator Series article: Unraveling the impact of template geometry and confinement on template-assisted self-assembly of nanoparticles. DOI:10.1039/D3ME00024A

This article will be free to access until September 15th! Read the interview with Shafigh below;

In your opinion, what are the most important questions to be asked/answered in this field of research?

The challenging task in this field is to thoroughly understand the mechanisms of Directed Self-Assembly (DSA) of Sub-50 nm Particle (abbreviated as SNP) arrays on templates at moving three-phase (solid- liquid-gas) interfaces, and utilize that understanding to investigate ways to manipulate DSA and ordering of assemblages with single particle precision (placing a single particle at every predefined position). The DSA process is a self-assembling system of discrete components, which is aided by utilizing stimuli such as external fields or templates for selective depositions. This process is currently considered as one of the main ways for nano-fabrication because of its simplicity, versatility, and spontaneity. DSA of SNP arrays onto 2D structures gives rise to novel collective properties that can be tuned by manipulating the external stimuli, interactions between SNPs, or particle size. Arrays of SNPs, such as semiconductor quantum dots, nanorods, and nanotetrapods, with such tunable properties have applications in electronics, magnetics, photonics, and catalysis. Ordered arrays of SNPs on template surfaces will also influence the fundamental nanoscale view of materials properties and the practical manifestation of bottom-up manufacturing technologies. SNPs are promising building blocks for nanotechnology due to their ease of fabrication and high degree of uniformity. However, this promise will be fulfilled only if a method to address single SNPs and their positioning in large arrays with desired arrangement on textured substrates is developed. To develop this method, a rigorous understanding of the mechanisms of DSA of SNP arrays on template surfaces is required.

Find out more about Dr Shafigh Mehraeen and their groups research on their website: https://tranzabi.people.uic.edu/

Reika Katsumata is an assistant professor in Polymer Science and Engineering Department at UMass Amherst since 2018. After obtaining her B.E. and M.E. degrees in Organic and Polymeric Materials at the Tokyo Institute of Technology, Reika made a big move to the States, where she earned Ph.D. degree in Chemical Engineering at the University of Texas at Austin. Before joining UMass, she completed her postdoctoral training at the University of California, Santa Barbara. Katsumata Group’s overarching research goal is to design extremely confined soft/hard interfaces, focusing on dynamics, wettability, and mechanics with a novel processing method of polymer-assisted rapid thermal annealing. She has been awarded the ACS petroleum research fund Doctoral New Investigator Grant (2019), NSF CAREER Award (2021), 3M Non-Tenure Faculty Award (2022), Japan Science and Technology Agency PRESTO Award (2022), AFOSR Young Investigator Award (2023), and ACS PMSE Early Stage Investigator Award (2023).

Read Reika’s Emerging Investigator article: Recent advances and emerging opportunities in rapid thermal annealing (RTA) of polymers. DOI: 10.1039/D2ME00283C

This article will be free to access for all until September 10th 2023! Read the interview with Reika below;

1. How do you feel about MSDE as a place to publish research on this topic?
   
   The interdisciplinary readership of MSDE has been the ideal audience for the topic of polymer-assisted rapid thermal annealing (RTA).
   2. What aspect of your work are you most excited about at the moment and what do you find most challenging about your research?As summarized in this review paper, I am overwhelmingly excited to be a part of the adventure in this new research field!
   
   3. In your opinion, what are the most important questions to be asked/answered in this field of research?How does the molecular structure influence the degradation behaviors of polymers under high heating rates?4. Can you share one piece of career-related advice or wisdom with other early career scientists?Pursue projects you are genuinely passionate about, rather than following hot topics.Follow Reika on twitter! @Katsumata_R_

Nick Jackson is an Assistant Professor in the Chemistry Department at the University of Illinois, Urbana-Champaign. He is a member of the Beckman Institute for Advanced Science and Technology, the Department of Chemical and Biological Engineering, and the Department of Materials Science and Engineering. Prof. Jackson has been recognized with several awards including the DOE Early Career Award, the Dreyfus Machine Learning in the Chemical Sciences and Engineering Award, and the 3M Nontenured Faculty Award. His research group integrates quantum mechanics, statistical mechanics, and machine learning to design electronic structure and reactivity in soft materials, with a focus on coarse-graining approaches to electronic structure, modeling mixed electron-ion conductivity, and predicting the reactivity of polymers. He received his B.A. (2011) in Physics from Wesleyan University, followed by a Ph.D. (2016) in Chemistry from Northwestern University. Prior to his appointment at UIUC, he was a Maria Goeppert Mayer Fellow and Assistant Scientist joint between Argonne National Laboratory and the University of Chicago.

Read Nick’s Emerging Investigator article, Electron and ion transport in semi-dilute conjugated polyelectrolytes: view from a coarse-grained tight binding model , DOI: 10.1039/D2ME00285J

1. How do you feel about MSDE as a place to publish research on this topic?
   
   I think that MSDE is an outstanding venue to publish research at the intersection of chemistry, materials science, and chemical engineering.2. What aspect of your work are you most excited about at the moment and what do you find most challenging about your research?

   At present I am most excited about trying to make quantum chemical predictions accessible to soft materials length scales (~10-100 nm), which are currently out of reach for traditional methods. These method development efforts typically require familiarity with state-of-the-art advances in machine learning, coarse-graining, and electronic structure calculations, which can be very difficult to keep on top of.
   
   3. In your opinion, what are the most important questions to be asked/answered in this field of research?

   The most important question that needs an answer in this field is if we can make coarse-grained electronic structure predictions as accurate and easy to use as traditional black-box quantum chemistry calculations. Time will tell!

   4. Can you share one piece of career-related advice or wisdom with other early career scientists?

   Follow the ideas that are YOUR ideas and that YOU are passionate about. Try not to let emerging research trends or fads distract you too much from what you are excited to be working on! The job is a lot more enjoyable when you are working on topics you love and not just trying to keep up with the Joneses.

Gary Koenig began an appointment as an Assistant Professor of Chemical Engineering at the University of Virginia in 2012, and was subsequently promoted to Associate Professor in 2018. In addition to teaching and professional service commitments, Gary is the principal investigator of a group which researches energy storage materials and systems. Gary completed a B.S. from The Ohio State University and then a Ph.D. from the University of Wisconsin-Madison, with both degrees in Chemical Engineering. Before starting at University of Virginia, Gary was a Postdoctoral Associate at Argonne National Laboratory. Gary has published over 50 research articles. His expertise span from probing fundamental materials properties to grid scale energy storage evaluation, and includes both experimental and simulation tools. Find out more about Gary’s research on his webpage Follow Gary on Twitter at @ChemEBattery

Read Gary Koenig’s Emerging Investigator article, ‘Improving high rate cycling limitations of thick sintered battery electrodes by mitigating molecular transport limitations through modifying electrode microstructure and electrolyte conductivity‘, DOI: 10.1039/D1ME00082A

1. How do you feel about MSDE as a place to publish research on this topic?

MSDE was a good fit for for this paper, given the breadth of the molecular systems audience and the applications/technology significance component of the journal scope, which fits well with the energy storage applications of our research.

2. What aspect of your work are you most excited about at the moment and what do you find most challenging about your research?

This work investigated an electrochemical system that has significant ion transport restrictions, and what is exciting is pushing the limits in such a system to understand how fast the overall electrochemical process can be driven. The most challenging aspect for this paper was identifying a suitable electrolyte to evaluate which improved the ion transport properties without causing other cell stability limitations.

3. In your opinion, what are the most important questions to be asked/answered in this field of research?

There are many in the broader field of battery research. Most specific to this research report is how to best optimize the trade-off between electrode designs that increase energy density while still maintaining reasonable charging times/rates. This ties in more broadly to other bigger challenges with regards to safety and reliability of battery cells.

Michael Webb is an Assistant Professor in the Chemical and Biological Engineering department at Princeton University, joining in 2020. He obtained a B.S. in Chemical Engineering from UC Berkeley in 2011 and his Ph.D in 2016, also in Chemical Engineering, for “Path-integral and Coarse-graining Strategies for Complex Molecular Phenomena.” Between 2016-2019, he performed postdoctoral research at the University of Chicago and Argonne National Laboratory on coarse-graining, enhanced sampling, and machine learning of polymeric materials. His group emphasizes computational approaches, including hierarchical simulation and machine learning, for understanding and designing polymeric systems for diverse applications. He is a recent recipient of the NSF CAREER award and a Howard B. Wentz Junior Faculty award at Princeton University.

Read Michael’s Emerging Investigator Series article, ‘Featurization Strategies for Polymer Sequence or Composition Design by Machine Learning’, DOI: 10.1039/D1ME00160D and check out his interview below

How do you feel about MSDE as a place to publish research on this topic?

MSDE was an appealing venue for us for a few different reasons. First, I appreciate the focus in scope at MSDE in that published articles need to specifically highlight a molecular design or optimisation strategy. Our work falls in the latter category, and our motivation for study is very much for the reason for enhancing the process of polymer sequence and/or composition design in the future. In addition, MSDE seems to welcome contributions at the interface of molecular science and machine learning, and so I thought the general readership would be good.

What aspect of your work are you most excited about at the moment and what do you find most challenging about your research?

I am particularly excited about designing triggered stimuli-response into polymers using tools from both molecular simulation and machine learning. The most pressing problem at the moment, in my view, is the inadequacy of underlying models of this behaviour and their chemical specificity. As a logistical challenge, I would say it is difficult to stay abreast of both relevant simulation and machine learning literature and effectively train my researchers to be competent and confident in both areas.

In your opinion, what are the most important questions to be asked/answered in this field of research?

I think we and others have been asking good questions in the field of polymer machine learning. What we need to do moving forward is address what our answers mean or how they need to change as we increase the complexity of our design tasks and the true real-world relevance of developed methods. There has been significant useful benchmarking and demonstrations for ‘model’ systems, but now we need to see how the rubber meets the road.

Can you share one piece of career-related advice or wisdom with other early career scientists?

Try to remember and reflect on why you are doing what you are doing, especially in times of high stress. This applies both for thinking about a specific research area but also more generally considering your career