Computational approaches play an increasingly important role in materials science, but improvements are much needed in their accuracy, speed, automation, and scaling to realistic systems and large computer resources. This talk will highlight challenges in atomistic modeling and illustrate how fundamental physics, chemistry, mathematics, and computer science can be combined to develop modeling methods for understanding and designing materials for energy technologies. In the domain of semiconductors, first-principles quantum calculations can extract the full details of interactions between electrons and phonons and quantitatively predict thermal and electrical transport properties of complex crystals. Once validated against experiment and automated, these computational methods have enabled discovery several previously nonexistent alloy compositions, that achieved record thermoelectric efficiency in devices. In the domain of polymer and crystalline ion conductors, molecular dynamics simulations enabled discovery of new classes of solid electrolytes and surprising anomalous phenomena in correlated liquids. Modeling of heterogeneous and reactive dynamics has long remained beyond reach due to the combined requirements of high accuracy and large scale of simulations.

New classes of methods, such as Bayesian active learning and deep equivariant neural networks, when trained on accurate quantum computations, have opened promising avenues for bringing near-quantum accuracy to systems of billions of atoms, reaching the scales needed for describing realistic materials. In the domain of heterogeneous catalysis and surface science, simulations of surface reconstruction and reaction phenomena approach the “digital twin” capability of correctly capturing what expensive experiments measure. A major remaining bottleneck is the fidelity of the underlying quantum calculations, where semilocal density functional approximations qualitatively miss key aspects of many-body electron exchange and correlation. Development of new types of orbital-dependent nonlocal functional approximations holds promise in breaking through the accuracy barrier, especially important for capturing charge transfer in defects, surfaces, and battery materials.