Research
My expertise is in first-principles calculations, solid-state chemistry, and machine learning. My goal is to leverage these skills to accelerate the timeline from conception to experimental realization of new and improved materials for renewable energy applications. My Ph.D. research focused on developing simple descriptors to predict solid-state stability at a fraction of the expense of traditional computational and experimental methods. As a postdoc, I’m working on understanding how we can model phenomena relevant to the synthesis and performance of battery materials and collaborating with experimentalists to test these theories in the lab.
Selected publications
Unveiling electronic structure requirements for multivalent battery cathodes
ACS Materials Letters, 2021, 3, 1213-1220
Toward automated materials synthesis platforms
Materials Horizons, 2021, 8, 2169-2198
Automating phase ID from XRD patterns with deep learning
Chemistry of Materials, 2021, 33, 11, 4204-4215
Working towards predictive solid-state synthesis with a detailed investigation into YBCO
Advanced Materials, 2021, 33, 24, 2100312
Testing ML models for formation energy on problems relevant to materials discovery
npj Computational Materials, 2020, 6 (97)
Thorough search for stable cesium chloride perovskite optoelectronics
Journal of the American Chemical Society, 2020, 142 (11)
New ternary nitrides and chemical bonding mechanism
Nature Materials, 2019, 18 (7)
Reinventing Goldschmidt’s tolerance factor for perovskite stability with machine learning
Simple model for the temperature-dependent thermodynamics of solids
Nature Communications, 2018, 9 (1)
