Prof. Daniel Tabor
Title: Building Physics-Based and Data-Driven Methods for Efficient Polymer Design and Spectroscopy Simulations
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. We focus on two types of materials: electronic polymers and intrinsically disordered proteins. Although radical-based polymers are promising 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. We will then discuss 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.
If time permits, the second part of the talk focuses on developing methods for accelerating the simulation and analysis of condensed phase spectroscopy. We present data-driven methods for computing condensed phase vibrational spectra of water directly from coarse-grained representations in a mixed quantum-classical framework. The talk will focus on model representation, development of robust physically motivated machine learning protocols, and the fidelity of the models over a range of conditions.
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, a Cottrell Scholar in 2023, and was awarded the NSF CAREER Award in 2023 and the Montague Teacher-Scholar award by Texas A&M in 2023.
Host: Prof. Ned Sibert