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RESEARCH PAPER · Scientific Data · 2020

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

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

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The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for moleculesOriginalPredicting Molecular Energy Using Force-Field Optimized Geometries and Atomic Vector Representations Learned from an Improved Deep Tensor Neural Network39Physical machine learning outperforms "human learning" in Quantum Chemistry3Neural Network Based in Silico Simulation of Combustion Reactions18TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials337QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules112Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy72Dictionary of 140k GDB and ZINC derived AMONs3Machine learning for chemical discovery183Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration135On the role of gradients for machine learning of molecular energies and forces137Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation288Ab initio machine learning in chemical compound space4A transferable active-learning strategy for reactive molecular force fields91Combined Graph/Relational Database Management System for Calculated Chemical Reaction Pathway Data14QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules142Bond order predictions using deep neural networks16Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization9Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning112Dataset Construction to Explore Chemical Space with 3D Geometry and Deep Learning28Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence151Organic reactivity from mechanism to machine learning175The Rise of Neural Networks for Materials and Chemical Dynamics92Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems840Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge34Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies20The machine-learned radii of atoms8Learning molecular potentials with neural networks53Teaching a neural network to attach and detach electrons from molecules126Ab Initio Machine Learning in Chemical Compound Space195Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition11Quantum Mechanics Enables "Freedom of Design" in Molecular Property Space1Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries196OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy88Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE121Artificial intelligence-enhanced quantum chemical method with broad applicability143Improving the Silicon Interactions of GFN-xTB11Δ-Quantum machine learning for medicinal chemistry0Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions29MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations7NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces134Prediction of protein p K a with representation learning54Δ-Quantum machine-learning for medicinal chemistry58Operando Characterization of Organic Mixed Ionic/Electronic Conducting Materials148Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential29Zeo-1, a computational data set of zeolite structures10VIB5 database with accurate ab initio quantum chemical molecular potential energy surfaces11Fast and Accurate Quantum Mechanical Modeling of Large Molecular Systems Using Small Basis Set Hartree–Fock Methods Corrected with Atom-Centered Potentials14High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set27SAIBench: Benchmarking AI for Science7Small-Basis Set Density-Functional Theory Methods Corrected with Atom-Centered Potentials20Machine Learning in Computational Chemistry1Accurate Deep Learning-Aided Density-Free Strategy for Many-Body Dispersion-Corrected Density Functional Theory26Computational methods to simulate molten salt thermophysical properties88QMugs, quantum mechanical properties of drug-like molecules144Global optimization of atomic structure enhanced by machine learning72Computer-aided drug design, quantum-mechanical methods for biological problems48Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics62Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery73Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory35High-Dimensional Neural Network Potential for Liquid Electrolyte Simulations90Extending machine learning beyond interatomic potentials for predicting molecular properties167FINETUNA: fine-tuning accelerated molecular simulations36A Transferrable Range-Separated Force Field for Water: Combining the Power of Both Physically-Motivated Models and Machine Learning Techniques0WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets0Improving semiempirical quantum mechanical methods with machine learning4Learning from multiple quantum chemical methods: Δ-learning, transfer learning, co-kriging, and beyond16QDataSet, quantum datasets for machine learning40Exploring the frontiers of chemistry with a generalreactive machine learning potential11NeuralNEB—neural networks can find reaction paths fast38A transferrable range-separated force field for water: Combining the power of both physically-motivated models and machine learning techniques18Data science techniques in biomolecular force field development21Exploring the frontiers of chemistry with a generalreactive machine learning potential3An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules6Quantum and anharmonic effects in hydrogen-bonded crystals, surfaces and biological systems0Neural network potentials for chemistry: concepts, applications and prospects128“Freedom of design” in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties16Computational and data-driven modelling of solid polymer electrolytes21Neural network atomistic potentials for global energy minima search in carbon clusters7Neural network potentials for reactive chemistry: CASPT2 quality potential energy surfaces for bond breaking13Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects44Transfer learning for chemically accurate interatomic neural network potentials48SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials200An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation20QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery46WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets24Machine Learning Interatomic Potentials and Long-Range Physics223Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states23Uncertainty-driven dynamics for active learning of interatomic potentials159MolTaut: A Tool for the Rapid Generation of Favorable Tautomer in Aqueous Solution16Machine Learning and Informatics Based Elucidation of Reaction Pathways for Upcycling Model Polyolefin to Aromatics11Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls34Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials22MF-PCBA: Multifidelity High-Throughput Screening Benchmarks for Drug Discovery and Machine Learning32Exploring the frontiers of chemistry with a generalreactive machine learning potential9Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins14Lightweight and effective tensor sensitivity for atomistic neural networks23Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ) on Tetrathiafulvalene (TTF)17Statistics and Bias-Free Sampling of Reaction Mechanisms from Reaction Network Models3Current and future machine learning approaches for modeling atmospheric cluster formation18Clusterome: A Comprehensive Data Set of Atmospheric Molecular Clusters for Machine Learning Applications17Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials22Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies23Evaluation of the MACE force field architecture: From medicinal chemistry to materials science196An Automated On-The-Fly Optimization of Resource Allocation for High-Throughput Protein-Ligand Binding Free Energy Simulations3Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials—A Review82Chemically accurate simulations by machine learning correlated approximations0Elucidation of collagen amino acid interactions with metals (B, Ni) encapsulated graphene/PEDOT material: Insight from DFT calculations and MD simulation32PubChemQC B3LYP/6-31G*//PM6 Data Set: The Electronic Structures of 86 Million Molecules Using B3LYP/6-31G* Calculations64Treating Semiempirical Hamiltonians as Flexible Machine Learning Models Yields Accurate and Interpretable Results28Synergy of semiempirical models and machine learning in computational chemistry22Advancing Large-Scale Molecular Dynamics through Machine Learning0Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space9Neural scaling of deep chemical models123Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences13Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope9Minimal Peptoid Dynamics Inform Self-Assembly Propensity6Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules33Towards physics-informed explainable machine learning and causal models for materials research34TrIP─Transformer Interatomic Potential Predicts Realistic Energy Surface Using Physical Bias9A physics-aware neural network for protein–ligand interactions with quantum chemical accuracy11AI in computational chemistry through the lens of a decade-long journey30Benchmarking Practices for ML Interatomic Potentials: A Critical Review of Methodological Pitfalls and What Was Missed (2017–2023)0Global geometry of chemical graph neural network representations in terms of chemical moieties13The emergence of machine learning force fields in drug design30Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules1Quantum-level machine learning calculations to predict the PES of Selegiline0Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting134Incorporating Neural Networks into the AMOEBA Polarizable Force Field14Training Transferable Interatomic Neural Network Potentials for Reactive Chemistry: Improved Chemical Space Sampling2Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential135Coupled cluster finite temperature simulations of periodic materials via machine learning11Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations147Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine9Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials55Accurate neural-network-based fitting of full-dimensional N 2 –Ar and N 2 –CH 4 two-body potential energy surfaces aimed at spectral simulations5Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach30TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations63RETRACTED ARTICLE: Comparing ANI-2x, ANI-1ccx neural networks, force field, and DFT methods for predicting conformational potential energy of organic molecules19Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential8Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters14Software Infrastructure for Next-Generation QM/MM−ΔMLP Force Fields32Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules38OBIWAN: An Element-Wise Scalable Feed-Forward Neural Network Potential6Quantum-level machine learning calculations of Levodopa3Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning46FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials16Benchmarking Quantum Mechanical Levels of Theory for Valence Parametrization in Force Fields15A Euclidean transformer for fast and stable machine learned force fields79Active causal learning for decoding chemical complexities with targeted interventions9Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization2

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