Solid-state materials make up the active components in critical technologies including semiconductors, batteries, and countless more. New materials are needed to meet the shifting demands of the 21st century and computational methods are playing a huge role in helping us find these alternative candidates.
The combinatorics of solid-state inorganic materials present a remarkable opportunity for the discovery of new compounds but a daunting challenge for doing so efficiently. These materials make use of the entirety of the periodic table and can manifest in thousands of unique compositions and structures, leading to billions of plausible hypothetical materials.
Our group is using computational methods to tackle materials design challenges in three primary areas:
Materials Discovery and Synthesis
Computational materials scientists have made great strides toward the inverse design of inorganic solids for targeted applications, but a major bottleneck is their successful synthesis.
We are leveraging ab initio thermodynamics along with machine learning approaches to guide the discovery and synthesis of new materials. Through close collaboration with experimental groups, we are working to understand phase transformations in the solid state and during vapor deposition of thin films.
Redox Materials for Energy Conversion and Storage
Redox materials cycle between two states (reduced and oxidized) by the removal and uptake of ions and/or electrons. Our group is interested in how the electronic structure of materials dictates their propensity to undergo reversible redox. We are currently working with experimental collaborators to develop new high-capacity cathode materials for Li-ion batteries and new approaches for sustainable hydrogen generation.
Recent papers on this topic:
AI for Materials Science
Artificial intelligence (AI) and machine learning (ML) are making waves in nearly every scientific discipline. Our group is working to establish these approaches as mainstream tools for computational materials scientists. Current areas of interest for the group are generative models for materials discovery, topological data analysis for materials, and pushing the boundaries of atomistic simulations with machine learning potentials.
Recent papers on this topic:
