mlsanityData to 14 Oct 2026

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

arXiv 2507.03853 ↗First posted 5 Jul 2025cs.LG physics.chem-phcited by 5

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Abstract (arXiv)

We introduce OrbitAll, a geometry- and physics-informed deep learning framework that encodes any molecular system with arbitrary charges, spins, and environmental effects using electronic structure information. It utilizes spin-polarized orbital features from the underlying quantum mechanical method and combines them with SE(3)-equivariant graph neural networks. OrbitAll demonstrates superior performance and generalization in predicting charged, open-shell, and solvated molecules, and robustly extrapolates to molecules significantly larger than the training data. OrbitAll achieves chemical accuracy using 10 times fewer training data than competing AI models, with approximately 103 - 104 speedup compared to density functional theory. Trained on a chemically diverse dataset, OrbitAll performs robustly on challenging molecular systems, and outperforms the foundational machine-learned interatomic potential, UMA, for highly charged species, despite using 35 times less molecular data and a 50-times-smaller model. After learning solvent effects, it accurately predicts solvent-dependent reaction pathways at about 100 times lower cost than explicit-solvation simulations using UMA.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 24 of its 91 references that are in the map. First posted on arXiv 5 Jul 2025.

Builds on

24 of 91 references.

References · 24

  1. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 746
  2. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  3. 2020Directional Message Passing for Molecular Graphscited by 561
  4. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
  5. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 441
  6. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 256
  7. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 255
  8. 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 204
  9. 2025UMA: A Family of Universal Models for Atomscited by 105
  10. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 104
  11. 2020OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Featurescited by 64
  12. 2021Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistrycited by 30
12 more
  1. 2024The dark side of the forces: assessing non-conservative force models for atomistic machine learningcited by 24
  2. 2023Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materialscited by 22
  3. 2020Machine learning of solvent effects on molecular spectra and reactionscited by 14
  4. 2019A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Moleculescited by 9
  5. 2024Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solventscited by 7
  6. 2021Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivitycited by 6
  7. 2020Multi-task learning for electronic structure to predict and explore molecular potential energy surfacescited by 5
  8. 2023AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learningcited by 5
  9. 2019Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learningcited by 4
  10. 2022Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Spacecited by 3
  11. 2024Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributescited by 3
  12. 2022Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regressioncited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Learning from the electronic structure of molecules across the periodic tablecited by 7
  2. 2026Learning the Kohn-Sham map with neural operators for quasi-linear scaling density functional theorycited by 2
  3. 2025Predictive Free Energy Simulations Through Hierarchical Distillation of Quantum Hamiltonianscited by 0
  4. 2026MALOQ: Massively Accelerated Learning of Operators for Quantum Transportcited by 0
  5. 2026Beyond DFT electronic band structures via dual-space orbital learningcited by 0