| Abstract Scope |
The complex interplay of chemical reactions, defect chemistry, solvation dynamics, transport phenomena, and structural evolution in multi-component materials underpin much of energy capture, conversion and storage. Understanding these dynamical processes at angstrom-to-mesoscopic scales is critical for advances in energy technologies; yet, such knowledge remains in its infancy. Here, we demonstrate how a synergistic integration of big data-analytics, machine learning (ML), first-principles calculations, and ab initio/classical reactive molecular dynamics simulations can address this knowledge gap. Specifically, ML frameworks enable automated development of accurate, robust, and transferable atomic-scale interaction models for a wide range of materials systems (e.g., ceramics, metals, 2D materials, water) using large datasets obtained from first principles. In addition, deep learning can be leveraged to enhance accuracy of low-fidelity electronic structure methods to approach coupled cluster methods; at much faster speeds. The predictive power of these models will be discussed in the context of computational discovery of functional materials. |