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 chemical reactions. 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/redox-active polymers and intrinsically disordered polymers. 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 multiscale simulations of the polymers. Next, we will discuss our efforts to use reinforcement learning methods to accelerate materials design. We are able to couple these methods directly with high-throughput computational simulation tools to accelerate the design process. Our initial demonstrations of this method are on optoelectronic organic materials design.
If time permits, we will discuss our work to understand the fundamental design principles for optimizing chemical reactions under external forces (mechanochemistry). Here, we use a combination of high-throughput screening, optimization methods, and graph-based neural network potentials to conduct a broad search for reactions that can be significantly accelerated by external forces that are achievable in modern mechanochemical reactors. Our methods use machine learning potentials in combination with reaction path searching protocols (e.g., nudged elastic band and the growing string methods) to find potential transition states. We then explore candidate “activatable” coordinates—specific deformation modes that lead to enhanced reaction rates—by analyzing a mix of localized and normal coordinates. The most promising reactions and degrees of motion are then verified by higher-level calculations.
Bio:
Daniel Tabor received his B.S. in Chemistry from the University of Texas at Austin in 2011. He then attended the University of Wisconsin—Madison for his Ph.D. (2016). From 2016-2019, he was a postdoc 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, with a research group that primarily focuses on organic materials design, developing new computational spectroscopy methods, and scientific machine learning methods. He was named a Texas A&M Institute of Data Science Career Initiation Fellow in 2021, a Cottrell Scholar in 2023, was awarded the NSF CAREER award (2023) and the Montague Teacher-Scholar award by the Texas A&M Center for Teaching Excellence (2023).
The Physics Frontiers Centers (PFC) program supports university-based centers and institutes where the collective efforts of a larger group of individuals can enable transformational advances in the most promising research areas. The program is designed to foster major breakthroughs at the intellectual frontiers of physics by providing needed resources such as combinations of talents, skills, disciplines, and/or specialized infrastructure, not usually available to individual investigators or small groups, in an environment in which the collective efforts of the larger group can be shown to be seminal to promoting significant progress in the science and the education of students. PFCs also include creative, substantive activities aimed at enhancing education, broadening participation of traditionally underrepresented groups, and outreach to the scientific community and general public.