Daniel Tabor

Research Interests

The discovery and development of advanced materials currently require an enormous amount of effort and time. Our group works to develop the necessary theoretical and computational tools required to create the next generation paradigm for materials discovery. The goal is to realize orders of magnitude increases in discovery rates compared to current methods. Topics of particular interest to the group include:

• Development of physics-based machine learning models that enable accelerated simulation of new materials across large length and time scales
• Fundamental studies of model systems to quickly learn essential guiding design principles for functional materials
• Developing robust data infrastructure, analysis, informatics, and optimization techniques that maximize both our and our collaborators' ability discover the "needles in the haystack" (i.e. great materials) of chemical space

We aim to develop techniques and algorithms that are modular and can be adapted to new materials problems that arise. Our current applications of interest include:

• Synthesizable organic molecules that have unique and useful electronic structures
• Redox-active polymers for energy storage applications
• Materials and/or combinations of materials with "interesting" phase diagrams