Equivariant Electronic Hamiltonian Prediction with Many-Body Message Passing

TL;DR

The paper introduces MACE-H, a graph neural network model that efficiently predicts electronic Hamiltonians with high accuracy, transferability, and suitability for high-throughput materials screening.

Contribution

It presents a novel high-body-order message passing GNN that captures local chemical environments and irreducible representations for accurate electronic property predictions.

Findings

Achieves sub-meV prediction errors on matrix elements.

Demonstrates high transferability across diverse materials.

Efficiently captures complex local chemical environments.

Abstract

Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. For large-scale applications, an ideal model would exhibit high generalization ability and computational efficiency. Here, we introduce the MACE-H graph neural network, which combines high body-order message passing with a node-order expansion to efficiently obtain all relevant $O(3)$ irreducible representations. The model achieves high accuracy and computational efficiency and captures the full local chemical environment features of, currently, up to $f$ orbital matrix interaction blocks. We demonstrate the model's accuracy and transferability on several open materials benchmark datasets of two-dimensional materials and a new dataset for bulk gold, achieving sub-meV prediction errors on matrix elements and high accuracy on eigenvalues across all systems. We further analyze the interplay of high-body-order message passing and locality that makes this model a good candidate for high-throughput material screening.