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

Abstract

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. \textsc{OrbNet} 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, \textsc{OrbNet} predicts energies within chemical accuracy of DFT at a computational cost that is thousand-fold or more reduced.

References in corpus (9)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Semi-Supervised Classification with Graph Convolutional Networks
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Long-Range Corrected Hybrid Density Functionals with Improved Dispersion Corrections
• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
• Accurate sampling using Langevin dynamics
• A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
• Ground state energy functional with Hartree-Fock efficiency and chemical accuracy
• Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond

Cited by in corpus (49)

• Atomistic Line Graph Neural Network for Improved Materials Property Predictions
• Physics-inspired structural representations for molecules and materials
• SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
• A Comprehensive Survey on Deep Graph Representation Learning
• Provably efficient machine learning for quantum many-body problems
• Perspective on integrating machine learning into computational chemistry and materials science
• Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems
• Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
• Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
• Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting
• Advances of Machine Learning in Materials Science: Ideas and Techniques
• OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
• Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry
• Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential
• Graph Neural Networks for Molecules
• Materials Representation and Transfer Learning for Multi-Property Prediction
• Physically inspired deep learning of molecular excitations and photoemission spectra
• Universal QM/MM Approaches for General Nanoscale Applications
• Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
• Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
• Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states
• Deep Coarse-grained Potentials via Relative Entropy Minimization
• Improved machine learning algorithm for predicting ground state properties
• Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
• On the design space between molecular mechanics and machine learning force fields
• Deep learning probability flows and entropy production rates in active matter
• Formula graph self-attention network for representation-domain independent materials discovery
• Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space
• An orbital-based representation for accurate Quantum Machine Learning
• 3DReact: Geometric deep learning for chemical reactions
• SPA${}^{\mathrm{H}}$M: the Spectrum of Approximated Hamiltonian Matrices representations
• Accurate quantum Monte Carlo forces for machine-learned force fields: Ethanol as a benchmark
• Local minima in quantum systems
• DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
• Unified Deep Learning Framework for Many-Body Quantum Chemistry via Green's Functions
• Decomposing Chemical Space: Applications to the Machine Learning of Atomic Energies
• Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
• Mean-Field Density Matrix Decompositions
• Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation
• Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression
• Low-data deep quantum chemical learning for accurate MP2 and coupled-cluster correlations
• SPA${}^{\mathrm{H}}$M(a,b): encoding the density information from guess Hamiltonian in quantum machine learning representations
• Shadow molecular dynamics and atomic cluster expansions for flexible charge models
• Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning
• Predicting trajectory behaviour via machine-learned invariant manifolds
• Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions
• Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors
• Machine learning of electronic structure and atomistic properties from the external potential