Abstract: Accurate atomistic computations of transport and reaction dynamics are an important challenge and an opportunity for designing materials for energy conversion and storage. In the context of thermoelectric materials, we develop new automatable computational methods for describing electron-phonon scattering dynamics. By predicting electrical transport properties, we computationally discovered several new low-cost thermoelectric alloys with record device performance. In the context of solid-state batteries, computations of ionic transport reveal how strong ionic interactions lead to disorder and surprising collective phenomena in amorphous polymer electrolyte materials and enable us to design new electrolyte chemistries.

High-fidelity ab-initio simulations of atomistic dynamics are limited to small systems and short times, and development of surrogate machine learning models for force fields is an emerging promising direction to access long-time large-scale dynamics of complex materials systems. However, the main challenges are high accuracy, reliability, and computational efficiency of these models, which critically depend on the training data sets. We develop ML interatomic potential models that are interpretable and uncertainty-aware, and orders of magnitude faster than reference quantum methods. Principled uncertainty quantification built into these models enables the construction of autonomous data acquisition schemes using active learning. We demonstrate on-the-fly learning of machine learning force fields and use them to gain insights into previously inaccessible physical and chemical phenomena in ion conductors, catalytic surface reactions, 2D materials phase transformations, and shape memory alloys.