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RESEARCH PAPER · Chemical Reviews · 2021

Ab Initio Machine Learning in Chemical Compound Space

Bing Huang, O. Anatole von Lilienfeld

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Ab Initio Machine Learning in Chemical Compound SpaceOriginalMachine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation52Machine learning based energy-free structure predictions of molecules, transition states, and solids98Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space69Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions3High-throughput oxygen chemical potential engineering of perovskite oxides for chemical looping applications110Latent Space Collapse in Materials Representation Learning: A Systems-Level Conceptual Analysis0Machine learning for design principles for single atom catalysts towards electrochemical reactions116Multi-fidelity prediction of molecular optical peaks with deep learning93Selected machine learning of HOMO–LUMO gaps with improved data-efficiency49SPA H M: the spectrum of approximated Hamiltonian matrices representations19The way to AI-controlled synthesis: how far do we need to go?13Uncertainty quantification for predictions of atomistic neural networks28Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning127Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential29Inclusion of More Physics Leads to Less Data: Learning the Interaction Energy as a Function of Electron Deformation Density with Limited Training Data12REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems76Bridging informatics and medicinal inorganic chemistry: Toward a database of metallodrugs and metallodrug candidates34Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning142Applying neural network force field on water nucleation0Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network43Challenges and Opportunities for Renewable Ammonia Production via Plasmon‐Assisted Photocatalysis62Heuristics and Uncertainty Quantification in Rational and Inverse Compound and Catalyst Design3Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods53Fermi Softness: A Local Perspective on Surface Activity2Accelerating quantum molecular simulations0Mechanistic Inference from Statistical Models at Different Data-Size Regimes37Computer-aided drug design, quantum-mechanical methods for biological problems48Ab initio machine learning of phase space averages18Using Deep 1D Convolutional Grated Recurrent Unit Neural Network to Optimize Quantum Molecular Properties and Predict Intramolecular Coupling Constants of Molecules of Potential Health Medications and Other Generic Molecules8Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Chemical Multiverse: An Expanded View of Chemical Space86Graph-based Automated Macro-Molecule Assembly0cell2mol: encoding chemistry to interpret crystallographic data34Metric learning for kernel ridge regression: assessment of molecular similarity12Physics-based representations for machine learning properties of chemical reactions33Large dataset of molecular and reaction energies from multi-level quantum chemical methods1Large dataset of molecular and reaction energies from multi-level quantum chemical methods0Cheap Turns Superior: A Linear Regression-Based Correction Method to Reaction Energy from the DFT4Neural network potentials8SELFIES and the future of molecular string representations261Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis51Recommender system for discovery of inorganic compounds11GPU-accelerated approximate kernel method for quantum machine learning16Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization40Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches44Design of Light-Metal Alloys Using Machine Learning Techniques0Recent advances in machine learning for electronic excited state molecular dynamics simulations2Transition1x - a dataset for building generalizable reactive machine learning potentials123Tuning OER Electrocatalysts toward LOM Pathway through the Lens of Multi-Descriptor Feature Selection by Artificial Intelligence-Based Approach51“Freedom of design” in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties16Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials36Automatic 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interpretable machine-learning potentials86Intermolecular pair potentials and force fields0Reply to Comment on ‘Physics-based representations for machine learning properties of chemical reactions’6Universal machine learning for the response of atomistic systems to external fields72Integrated Molecular Modeling and Machine Learning for Drug Design70MultiXC-QM9: Large dataset of molecular and reaction energies from multi-level quantum chemical methods31Unlocking the predictive power of quantum-inspired representations for intermolecular properties in machine learning3La Era Digital en la Química Farmacéutica: Transformando el Diseño de Medicamentos con Métodos Computacionales0Accelerated chemical science with AI86Optimization of microwave-assisted extraction in the purification of triglycerides from non-edible crude Calophyllum inophyllum oil as biodiesel feedstock using artificial intelligence16Advances in machine intelligence‐driven virtual screening approaches for big‐data32Automated prediction of ground state spin for transition metal complexes13Benchmarking machine-readable vectors of chemical reactions on computed activation barriers9Big data benchmarking: how do DFT methods across the rungs of Jacob's ladder perform for a dataset of 122k CCSD(T) total atomization energies?13Electrostatic embedding machine learning for ground and excited state molecular dynamics of solvated molecules25Human-in-the-Loop without the Hype: A Conceptual Taxonomy of Human Roles in Materials AI1Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning70SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations10ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT36AI-Based Nano-Scale Material Property Prediction for Li-Ion Batteries4Some Recent Advances in Density-Based Reactivity Theory40Optimized multifidelity machine learning for quantum chemistry16Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks39Making quantum chemistry compressive and expressive: Toward practical ab‐initio simulation4The Importance of Reaction Energy in Predicting Chemical Reaction Barriers with Machine Learning Models6Directional multiobjective optimization of metal complexes at the billion-system scale42Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments139From Synthesis Conditions to UiO-66 Properties: Machine Learning Approach15In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back783DReact: Geometric Deep Learning for Chemical Reactions24Modeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes74Interpretable AI and Machine Learning Classification for Identifying High-Efficiency Donor–Acceptor Pairs in Organic Solar Cells11Quantum Topological Atomic Properties of 44K molecules5AGAPE (computAtional G-quadruplex Affinitiy PrEdiction): The first Artificial Intelligence workflow for G-quadruplex binding affinity prediction0Exploring Cationic Substitutions in the Solid Electrolyte NaAlCl4 with Density Functional Theory6Chemical Reaction Prediction using Machine Learning4Data Generation for Machine Learning Interatomic Potentials and Beyond176Introduction to machine learning potentials for atomistic simulations35Transferability of atom-based neural networks1Simple and Efficient Equivariant Message-Passing Neural Network Model for Non-local Potential Energy Surfaces7X2-GNN: A Physical Message Passing Neural Network with Natural Generalization Ability to Large and Complex Molecules9Deep learning-driven prediction of chemical addition patterns for carboncones and fullerenes2Machine Learning Mapping Approach for Computing Spin Relaxation Dynamics12AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic 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learning22Accurate Neural Network Fine-Tuning Approach for Transferable Ab Initio Energy Prediction across Varying Molecular and Crystalline Scales8Dual-Level Parametrically Managed Neural Network Method for Learning a Potential Energy Surface for Efficient Dynamics1Database of Nonaqueous Proton-Conducting Materials1Three decades of quantum science: how quantum chemistry transformed thermochemical database generation for benchmarking DFT and machine learning9Transferability Across Different Molecular Systems and Levels of Theory with the Data-Driven Coupled-Cluster Scheme5Predicting Solid-state NMR Observables via Machine Learning2Integer linear programming for unsupervised training set selection in molecular machine learning0Practical Application of Quantum-Enhanced Machine Learning0Cartesian atomic moment machine learning interatomic potentials9Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials17Efficient moment tensor machine-learning interatomic potential for accurate description of defects in Ni-Al Alloys8Exploring Effects of Modified Machine Learning Pipelines of Astrochemical Inventories6MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules231Machine learning-assisted prediction and optimization of electrocatalytic activity16Studying Noncovalent Interactions in Molecular Systems with Machine Learning56Geometric Guidance Integrated with Directed Electrostatics Strategy within a Graph Neural Network Approach for Nanocluster Structure Prediction2Applications of machine learning in heavy metal adsorption modeling: A review42Prediction of material properties of energetic materials using machine learning methods1Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective14Big‐Data Analysis of Geometric Descriptors as Efficient Predictors of Energetic Stability in Nonplanar Polycyclic Aromatic Hydrocarbons3Augmenting Chemical Databases for Atomistic Machine Learning by Sampling Conformational Space3

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  1. Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation

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  2. Machine learning based energy-free structure predictions of molecules, transition states, and solids

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  4. Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions

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  50. “Freedom of design” in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties

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