OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems

• URL: http://arxiv.org/abs/2507.03853v1
• Date: Sat, 05 Jul 2025 01:21:56 GMT
• Title: OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems
• Authors: Beom Seok Kang, Vignesh C. Bhethanabotla, Amin Tavakoli, Maurice D. Hanisch, William A. Goddard III, Anima Anandkumar,
• Abstract summary: OrbitAll is a geometry- and physics-informed deep learning framework.<n>It can represent and process any molecular system with arbitrary charges, spins, and environmental effects.<n>It achieves chemical accuracy using 10 times fewer training data than competing AI models, with a speedup of approximately $103$ - $104$ compared to density functional theory.
• Score: 64.69217059173184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the success of deep learning methods in quantum chemistry, their representational capacity is most often confined to neutral, closed-shell molecules. However, real-world chemical systems often exhibit complex characteristics, including varying charges, spins, and environments. We introduce OrbitAll, a geometry- and physics-informed deep learning framework that can represent all molecular systems with electronic structure information. OrbitAll utilizes spin-polarized orbital features from the underlying quantum mechanical method, and combines it with graph neural networks satisfying SE(3)-equivariance. The resulting framework can represent and process any molecular system with arbitrary charges, spins, and environmental effects. OrbitAll demonstrates superior performance and generalization on predicting charged, open-shell, and solvated molecules, while also robustly extrapolating to molecules significantly larger than the training data by leveraging a physics-informed architecture. OrbitAll achieves chemical accuracy using 10 times fewer training data than competing AI models, with a speedup of approximately $10^3$ - $10^4$ compared to density functional theory.

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