AI and the World's Hard Problems · Chemistry & materials · Nature Computational Science · 2026-10-13
HIP: Hessian Interatomic Potentials without derivatives
Molecular Hessians, the second derivatives of potential energy, are needed in many computational chemistry workflows but are expensive to compute and scale poorly with system size. The authors present HIP, a deep learning model that predicts Hessians directly, without automatic differentiation or finite differences, using SE(3)-equivariant features from a graph neural network. HIP is reported to be one to two orders of magnitude faster and more accurate than automatic-differentiation Hessians, and to perform well in transition-state search, geometry optimization and vibrational analysis.