ArticleProceedings of the National Academy of Sciences of the United States of America2022
Informing geometric deep learning with electronic interactions to accelerate quantum chemistry.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- Integrating Coupled-Cluster Theory Within AI Workflows for Accurate Molecular Geometries.Journal of computational chemistry · 2026Article
- Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states.Digital discovery · 2026Article
- Predictive free energy simulations through hierarchical distillation of quantum Hamiltonians.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials.Journal of chemical theory and computation · 2026Article
- Deep learning methods for 2D material electronic properties.Digital discovery · 2026Review
- Free-Energy Landscapes and Surface Dynamics in Methane Activation on Ni(511) via Machine Learning and Enhanced Sampling.ACS catalysis · 2025Article
- Image super-resolution inspired electron density prediction.Nature communications · 2025Article
- LAGNet: better electron density prediction for LCAO-based data and drug-like substances.Journal of cheminformatics · 2025Article
- Accurate Neural Network Fine-Tuning Approach for Transferable Ab Initio Energy Prediction across Varying Molecular and Crystalline Scales.Journal of chemical theory and computation · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023.Chemical science · 2025Article
- Unsupervised representation learning of Kohn-Sham states and consequences for downstream predictions of many-body effects.Nature communications · 2024Article
- A deep equivariant neural network approach for efficient hybrid density functional calculations.Nature communications · 2024Article
- Equivariant Neural Networks Utilizing Molecular Clusters for Accurate Molecular Crystal Lattice Energy Predictions.ACS omega · 2024Article
- Investigating the behavior of diffusion models for accelerating electronic structure calculations.Chemical science · 2024Article
- Unsupervised manifold embedding to encode molecular quantum information for supervised learning of chemical data.Communications chemistry · 2024Article
- Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.iScience · 2024Review
- Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.RSC advances · 2024Article
- Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.Nature communications · 2024Article
- Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities.The journal of physical chemistry. C, Nanomaterials and interfaces · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. However, existing machine learning techniques are challenged by the scarcity of training data when exploring unknown chemical spaces. We overcome this barrier by systematically incorporating knowledge of molecular electronic structure into deep learning. By developing a physics-inspired equivariant neural network, we introduce a method to learn molecular representations based on the electronic interactions among atomic orbitals. Our method, OrbNet-Equi, leverages efficient tight-binding simulations and learned mappings to recover high-fidelity physical quantities. OrbNet-Equi accurately models a wide spectrum of target properties while being several orders of magnitude faster than density functional theory. Despite only using training samples collected from readily available small-molecule libraries, OrbNet-Equi outperforms traditional semiempirical and machine learning-based methods on comprehensive downstream benchmarks that encompass diverse main-group chemical processes. Our method also describes interactions in challenging charge-transfer complexes and open-shell systems. We anticipate that the strategy presented here will help to expand opportunities for studies in chemistry and materials science, where the acquisition of experimental or reference training data is costly.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.