Abstract: Our research group focuses on building tools that enable inverse materials design and give new insights into the fundamental chemical physics of liquids, interfaces, and materials. For this talk, we will discuss our progress in two of our primary research thrusts.
The first part of the talk will focus on our work in developing methods that are used to accelerate the design of functional materials, including radical-based polymers and organic optoelectronic materials. The radical-based polymers have the potential to serve as energy storage materials. Successful materials design requires careful molecular engineering of the polymer and electrolyte. To solve the molecular-scale part of the problem, we develop physically motivated machine learning models that predict molecular properties (e.g., hole reorganization energies) from low-cost representations, and pair these with reinforcement learning methods for inverse design applications. In our first demonstration of the reinforcement learning scheme, we show that this framework is capable of integrating with quantum chemistry calculations in real-time, and through a careful design of the curriculum, we are able to find a diverse set of molecules with desired singlet and triplet energy levels.
The second part will focus on our efforts on developing representations for predicting the polymer physics of intrinsically disordered proteins at a much lower computational cost that current coarse-grained methods. One advantage of our new representation is that it avoids specifying the longest length of the chain in advance. In addition, this representation works well with a set of highly charged amino acid sequences, uncovering new insights to the fundamental interactions and scaling behavior of these systems.
Bio: Daniel Tabor received his B.S. in Chemistry from the University of Texas at Austin in 2011, where he was advised by John F. Stanton. He then attended the University of Wisconsin—Madison for his Ph.D. (2016), where he was a member of Ned Sibert’s group. From 2016-2019, he was a postdoc with Alán Aspuru-Guzik at Harvard University. Daniel began his independent career on the faculty at Texas A&M in the Fall of 2019, where he is currently an Assistant Professor in the Department of Chemistry. He was named a Texas A&M Institute of Data Science Career Initiation Fellow in 2021 and a Cottrell Scholar in 2023.