Data to 7 Oct 2026

OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features

arXiv 2007.08026 ↗First posted 15 Jul 2020physics.chem-ph cs.LGcited by 63

Not on the map. Why?

Abstract (arXiv)

We introduce a machine learning method in which energy solutions from the Schrodinger equation are predicted using symmetry adapted atomic orbitals features and a graph neural-network architecture. textscOrbNet is shown to outperform existing methods in terms of learning efficiency and transferability for the prediction of density functional theory results while employing low-cost features that are obtained from semi-empirical electronic structure calculations. For applications to datasets of drug-like molecules, including QM7b-T, QM9, GDB-13-T, DrugBank, and the conformer benchmark dataset of Folmsbee and Hutchison, textscOrbNet predicts energies within chemical accuracy of DFT at a computational cost that is thousand-fold or more reduced.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jul 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 18; 2022: 18; 2023: 15; 2024: 3; 2025: 7; 2026: 2.

63 papers.

Sort:

By area:

2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 201
2022Graph neural networks for materials science and chemistrycited by 181
2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
2021Spherical Message Passing for 3D Graph Networkscited by 166
2021Nested Graph Neural Networkscited by 136
2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
2021Provably efficient machine learning for quantum many-body problemscited by 91
2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
2021SE(3)-equivariant prediction of molecular wavefunctions and electronic densitiescited by 67
2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66

38 more

2022Boosting the Cycle Counting Power of Graph Neural Networks with I2-GNNscited by 55
2021Rotation Invariant Graph Neural Networks using Spin Convolutionscited by 52
2022Spherical Channels for Modeling Atomic Interactionscited by 47
2023Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltoniancited by 38
2022Machine Learning Coarse-Grained Potentials of Protein Thermodynamicscited by 37
2021Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functionscited by 36
2021Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistrycited by 27
2021Learning neural network potentials from experimental data via Differentiable Trajectory Reweightingcited by 26
2022Towards Training Billion Parameter Graph Neural Networks for Atomic Simulationscited by 25
2021Directional Message Passing on Molecular Graphs via Synthetic Coordinatescited by 24
2023MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Designcited by 23
2023Improved machine learning algorithm for predicting ground state propertiescited by 22
2023Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materialscited by 22
2022Graph Neural Networks for Moleculescited by 21
2022Sampling-free Inference for Ab-Initio Potential Energy Surface Networkscited by 19
2023Deep learning probability flows and entropy production rates in active mattercited by 15
2023Uncertainty Estimation for Molecules: Desiderata and Methodscited by 11
2023Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Powercited by 11
2025SciVideoBench: Benchmarking Scientific Video Reasoning in Large Multimodal Modelscited by 11
2025High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Predictioncited by 10
20223D Graph Contrastive Learning for Molecular Property Predictioncited by 9
2021Distributed Representations of Atoms and Materials for Machine Learningcited by 8
2021Geometric Transformer for End-to-End Molecule Properties Predictioncited by 8
2022NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechaniccited by 8
2023Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantificationcited by 8
2021Materials Representation and Transfer Learning for Multi-Property Predictioncited by 7
2021Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potentialcited by 7
2022Formula graph self-attention network for representation-domain independent materials discoverycited by 7
20233DReact: Geometric deep learning for chemical reactionscited by 7
20233D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Informationcited by 6
2024A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamicscited by 5
2025OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systemscited by 4
2025Scaling Laws and Symmetry, Evidence from Neural Force Fieldscited by 4
2021Molecular Energy Learning Using Alternative Blackbox Matrix-Matrix Multiplication Algorithm for Exact Gaussian Processcited by 3
2022Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Spacecited by 3
2024On the design space between molecular mechanics and machine learning force fieldscited by 3
2025BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Modelscited by 3
2021Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspectivecited by 2

50 most cited of 63 All 63 (JSON)