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E3STO: Orbital Inspired SE(3)-Equivariant Molecular Representation for Electron Density Prediction

arXiv 2410.06119 ↗First posted 8 Oct 2024physics.chem-ph cs.LG q-bio.BMcited by 0

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Abstract (arXiv)

Electron density prediction stands as a cornerstone challenge in molecular systems, pivotal for various applications such as understanding molecular interactions and conducting precise quantum mechanical calculations. However, the scaling of density functional theory (DFT) calculations is prohibitively expensive. Machine learning methods provide an alternative, offering efficiency and accuracy. We introduce a novel SE(3)-equivariant architecture, drawing inspiration from Slater-Type Orbitals (STO), to learn representations of molecular electronic structures. Our approach offers an alternative functional form for learned orbital-like molecular representation. We showcase the effectiveness of our method by achieving SOTA prediction accuracy of molecular electron density with 30-70% improvement over other work on Molecular Dynamics data.

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References · 15

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
  3. 2021E(n) Equivariant Graph Neural Networkscited by 951
  4. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  5. 2020Neural Operator: Graph Kernel Network for Partial Differential Equationscited by 722
  6. 2020Directional Message Passing for Molecular Graphscited by 556
  7. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  8. 2022e3nn: Euclidean Neural Networkscited by 171
  9. 2021SE(3)-equivariant prediction of molecular wavefunctions and electronic densitiescited by 67
  10. 2016By-passing the Kohn-Sham equations with machine learningcited by 65
  11. 2021Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solidscited by 24
  12. 2020DeepDFT: Neural Message Passing Network for Accurate Charge Density Predictioncited by 12
3 more
  1. 2023Equivariant Neural Operator Learning with Graphon Convolutioncited by 10
  2. 2024Gaussian Plane-Wave Neural Operator for Electron Density Estimationcited by 8
  3. 2024A Recipe for Charge Density Predictioncited by 8

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