A Framework of SO(3)-equivariant Non-linear Representation Learning and its Application to Electronic-Structure Hamiltonian Prediction

• URL: http://arxiv.org/abs/2405.05722v4
• Date: Tue, 15 Oct 2024 03:08:29 GMT
• Title: A Framework of SO(3)-equivariant Non-linear Representation Learning and its Application to Electronic-Structure Hamiltonian Prediction
• Authors: Shi Yin, Xinyang Pan, Fengyan Wang, Lixin He,
• Abstract summary: We propose a theoretical and a methodological framework to address a critical challenge in applying deep learning to physical systems. Inspired by covariant theory in physics, we present a solution by exploring the mathematical relationships between SO(3)-invariant and SO(3)-equivariant quantities. We show that our method boosts Hamiltonian prediction accuracy by up to 40% and enhances downstream physical quantities, such as occupied orbital energy, by a maximum of 76%.
• Score: 1.8982950873008362
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose both a theoretical and a methodological framework to address a critical challenge in applying deep learning to physical systems: the reconciliation of non-linear expressiveness with SO(3)-equivariance in predictions of SO(3)-equivariant quantities. Inspired by covariant theory in physics, we present a solution by exploring the mathematical relationships between SO(3)-invariant and SO(3)-equivariant quantities and their representations. We first construct theoretical SO(3)-invariant quantities derived from the SO(3)-equivariant regression targets, and use these invariant quantities as supervisory labels to guide the learning of high-quality SO(3)-invariant features. Given that SO(3)-invariance is preserved under non-linear operations, the encoding process for invariant features can extensively utilize non-linear mappings, thereby fully capturing the non-linear patterns inherent in physical systems. Building on this, we propose a gradient-based mechanism to induce SO(3)-equivariant encodings of various degrees from the learned SO(3)-invariant features. This mechanism can incorporate non-linear expressive capabilities into SO(3)-equivariant representations, while theoretically preserving their equivariant properties as we prove, establishing a strong foundation for regressing complex SO(3)-equivariant targets. We apply our theory and method to the electronic-structure Hamiltonian prediction tasks, experimental results on eight benchmark databases covering multiple types of systems and challenging scenarios show substantial improvements on the state-of-the-art prediction accuracy of deep learning paradigm. Our method boosts Hamiltonian prediction accuracy by up to 40% and enhances downstream physical quantities, such as occupied orbital energy, by a maximum of 76%.

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