AI-MI Seminar Series: Daniel Tabor (Texas A&M University)

Watch the recording: youtube.com/watch?v=H_kFO0HEJL8

Machine Learning Methods for Molecular Design and Reaction Discovery

This talk will discuss strategies we have developed for efficiently navigating chemical space, and the physical insights we gained during their design and deployment.

The first part will focus on our work in developing methods that accelerate the prediction of properties of molecules and materials at multiple length scales. This increased prediction speed allows for the high-throughput virtual screening of large chemical spaces (e.g., for organic electronics) and the simulation of materials at large length scales. We will discuss the logic behind developing these representations and how to make the models stronger and more transferable.

The second part will focus on our work for accelerating the discovery of new reactions for mechanochemistry. This involves using state of the art machine learning interatomic potentials in concert with robust physical models for describing primary mechanochemical processes. The scope of the work (over 250,000 Hessian evaluations) is only possible due to recent advances in these methods, and enables a much broader assessment of chemical spaces that can be accelerated with mechanochemical methods.

Daniel Tabor, Associate Professor, Texas A&M University

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 Tabor group works on developing new theoretical tools for accelerating computational materials design, simulating and analyzing the spectroscopy of condensed phase and interfacial systems, and characterizing the structure and dynamics of disordered biophysical systems.

Thursday, October 15, 2026, 10:00 – 11:00 am ET · Watch live on YouTube @AIMaterialsInstitute