Unified Differentiable Learning of Electric Response

Understand

Predicting response of materials to external stimuli is a primary objective of computational materials science.

• However, current methods are limited to small-scale simulations due to the unfavorable scaling of computational costs.
• Here, we implement an equivariant machine-learning framework where response properties stem from exact differential relationships between a generalized potential function and applied external fields.
• Focusing on responses to electric fields, the method predicts electric enthalpy, forces, polarization, Born charges, and polarizability within a unified model enforcing the full set of exact physical constraints, symmetries and conservation laws.