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RESEARCH PAPER · Nature Communications · 2019

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Justin S. Smith, Benjamin T. Nebgen, R.I. Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton M. Barros, Sergei Tretiak, Olexandr Isayev, Adrián E. Roitberg

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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learningOriginalAccurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network277Machine Learning Interatomic Potentials as Emerging Tools for Materials Science1036Autonomous Discovery in the Chemical Sciences Part I: Progress317Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning65Dataset’s chemical diversity limits the generalizability of machine learning predictions127Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground127Machine learning and artificial neural network accelerated computational discoveries in materials science151Integral-Direct and Parallel Implementation of the CCSD(T) Method: Algorithmic Developments and Large-Scale Applications81Opportunities for Artificial Intelligence in Advancing Precision Medicine105Generating transition states of isomerization reactions with deep learning79Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening108Simulating protein–ligand binding with neural network potentials90Automated in Silico Design of Homogeneous Catalysts186Machine learning for interatomic potential models363Monitoring sugar crystallization with deep neural networks61Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation78Machine-Learning Methods for Computational Science and Engineering223Quantum Chemistry in the Age of Machine Learning417Transforming solid-state precipitates via excess vacancies119Deep Learning for Optoelectronic Properties of Organic Semiconductors58Roadmap on multiscale materials modeling210Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction269Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique89Organic Photovoltaics: Relating Chemical Structure, Local Morphology, and Electronic Properties60The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules280Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide68Machine Learning in Chemistry77Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations86Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Exploring chemical compound space with quantum-based machine learning369Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens441Learning to Make Chemical Predictions: The Interplay of Feature Representation, Data, and Machine Learning Methods114TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials337Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition107Assessing conformer energies using electronic structure and machine learning methods82Transfer Learning for Drug Discovery366Quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules138AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials69Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials96AI Applications through the Whole Life Cycle of Material Discovery192Established and Emerging Computational Tools to Study Homogeneous Catalysis—From Quantum Mechanics to Machine Learning70Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy72Modelling and understanding battery materials with machine-learning-driven atomistic simulations114Ultrafast Spectroscopy of Photoactive Molecular Systems from First Principles: Where We Stand Today and Where We Are Going74Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening58OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features242Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates206Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications82Quantum chemical accuracy from density functional approximations via machine learning384A general-purpose machine-learning force field for bulk and nanostructured phosphorus144Emerging materials intelligence ecosystems propelled by machine learning441Ultra-fast and accurate binding energy prediction of shuttle effect-suppressive sulfur hosts for lithium-sulfur batteries using machine learning91Machine Learning for Electronically Excited States of Molecules398DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory87A critical overview of computational approaches employed for COVID-19 drug discovery203Data-efficient machine learning for molecular crystal structure prediction108Real-time prediction of 1 H and 13 C chemical shifts with DFT accuracy using a 3D graph neural network137TSNet: predicting transition state structures with tensor field networks and transfer learning62Accurate Reduced-Cost CCSD(T) Energies: Parallel Implementation, Benchmarks, and Large-Scale Applications79Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases62Active learning accelerates ab initio molecular dynamics on reactive energy surfaces93Predicting Density Functional Theory-Quality Nuclear Magnetic Resonance Chemical Shifts via Δ-Machine Learning76Pure non-local machine-learned density functional theory for electron correlation74When do short-range atomistic machine-learning models fall short?135Miniaturization of optical spectrometers950Δ -machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) level of theory154Efficient Amino Acid Conformer Search with Bayesian Optimization54Automated discovery of a robust interatomic potential for aluminum97Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning112Machine learning in materials science: From explainable predictions to autonomous design281Transfer learning for solvation free energies: From quantum chemistry to experiments170Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence151Serum Metabolomics Identifies Dysregulated Pathways and Potential Metabolic Biomarkers for Hyperuricemia and Gout128Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water180Deep learning for ultra-fast and high precision screening of energy materials55Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials68Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery55Breaking the Coupled Cluster Barrier for Machine-Learned Potentials of Large Molecules: The Case of 15-Atom Acetylacetone76Molecular excited states through a machine learning lens200Machine-learned potentials for next-generation matter simulations560Best practices in machine learning for chemistry505Machine Learning for Chemical Reactions437Perspective on integrating machine learning into computational chemistry and materials science203The Rise of Neural Networks for Materials and Chemical Dynamics92Machine Learning Force Fields: Recent Advances and Remaining Challenges127Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Learning molecular potentials with neural networks53Teaching a neural network to attach and detach electrons from molecules126Ab Initio Machine Learning in Chemical Compound Space195Learning protein-ligand binding affinity with atomic environment vectors70Gaussian Process Regression for Materials and Molecules1221Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution109OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy88Free Energy Methods in Drug Discovery—Introduction71Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries65Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting103Artificial intelligence-enhanced quantum chemical method with broad applicability143Resolving Chemical Dynamics in Biological Energy Conversion: Long-Range Proton-Coupled Electron Transfer in Respiratory Complex I75Artificial intelligence: machine learning for chemical sciences104End-to-end differentiable construction of molecular mechanics force fields88Predicting reaction conditions from limited data through active transfer learning78Prediction of protein p K a with representation learning54Reaction dynamics of Diels–Alder reactions from machine learned potentials54Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning309TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics54Using machine learning as a surrogate model for agent-based simulations133Phase transitions of zirconia: Machine-learned force fields beyond density functional theory62Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions66Deep dive into machine learning density functional theory for materials science and chemistry111Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-285Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties81Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics62Informing geometric deep learning with electronic interactions to accelerate quantum chemistry100High-Dimensional Neural Network Potential for Liquid Electrolyte Simulations90Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials58Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis140How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?80Coupled Cluster Molecular Dynamics of Condensed Phase Systems Enabled by Machine Learning Potentials: Liquid Water Benchmark101How machine learning can accelerate electrocatalysis discovery and optimization125Multi-objective Bayesian optimization accelerated design of TPMS structures68Deep learning metal complex properties with natural quantum graphs59SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials200The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts322The central role of density functional theory in the AI age237PubChemQC B3LYP/6-31G*//PM6 Data Set: The Electronic Structures of 86 Million Molecules Using B3LYP/6-31G* Calculations64NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics99High-resolution single-photon imaging with physics-informed deep learning74How to train a neural network potential79Modern Alchemical Free Energy Methods for Drug Discovery Explained106Orbital-Free Density Functional Theory: An Attractive Electronic Structure Method for Large-Scale First-Principles Simulations115Recent advances and outstanding challenges for machine learning interatomic potentials130OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials560State-of-the-art local correlation methods enable affordable gold standard quantum chemistry for up to hundreds of atoms68MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows72Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting134Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential135Pretraining of attention-based deep learning potential model for molecular simulation106In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back78Systematic assessment of various universal machine‐learning interatomic potentials64Data Generation for Machine Learning Interatomic Potentials and Beyond176Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials87DPA-2: a large atomic model as a multi-task learner152AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs203The evolution of machine learning potentials for molecules, reactions and materials71Foundation models for materials discovery – current state and future directions102The Amsterdam Modeling Suite156MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules231Advancing materials discovery through artificial intelligence68A foundation model for atomistic materials chemistry566Automated discovery of a robust interatomic potential for aluminum80

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