Daniel Tabor

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/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. 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.

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 the generalizability of this method to other molecular design tasks.

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