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RESEARCH PAPER · Nature Chemistry · 2021

Best practices in machine learning for chemistry

Nongnuch Artrith, Keith T. Butler, François‐Xavier Coudert, Seungwu Han, Olexandr Isayev, Anubhav Jain, Aron Walsh

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Best practices in machine learning for chemistryOriginalLattice oxygen redox chemistry in solid-state electrocatalysts for water oxidation706Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry147Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning70Effectively improving the accuracy of PBE functional in calculating the solid band gap via machine learning98AENET–LAMMPS and AENET–TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials29Topological methods for analysis and design of coordination polymers50Machine Learning for Functional Group Identification in Vibrational Spectroscopy: A Pedagogical Lab for Undergraduate Chemistry Students32Artificial Intelligence Applied to Battery Research: Hype or Reality?503New trends in nonconventional carbon dot synthesis60Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control68Bioactive Synthetic Polymers183Computational spectroscopy of complex systems73Developing Efficient Small Molecule Acceptors with sp2‐Hybridized Nitrogen at Different Positions by Density Functional Theory Calculations, Molecular Dynamics Simulations and Machine Learning139Modern nanoscience: Convergence of AI, robotics, and colloidal synthesis45Accelerated prediction of Cu-based single-atom alloy catalysts for CO2 reduction by machine learning77Natural product drug discovery in the artificial intelligence era219Accelerating colloidal quantum dot innovation with algorithms and automation30Identifying structure–absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics59Multi-fidelity prediction of molecular optical peaks with deep learning93Natural language processing models that automate programming will transform chemistry research and teaching59Structure–property correlation in oxide-ion and proton conductors for clean energy applications: recent experimental and computational advancements63Trusting our machines: validating machine learning models for single-molecule transport experiments32Deep learning meets quantitative structure–activity relationship (QSAR) for leveraging structure-based prediction of solute rejection in organic solvent nanofiltration99Computational prediction of plant metabolic pathways28Machine Learning Guided Synthesis of Flash Graphene110Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte33A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra72A review of the recent progress in battery informatics178Use of machine learning in osteoarthritis research: a systematic literature review80Robust Design of High-Performance Optoelectronic Chalcogenide Crystals from High-Throughput Computation60Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling189Artificial intelligence to bring nanomedicine to life189A Brief Review of Machine Learning-Based Bioactive Compound Research28The transformational role of GPU computing and deep learning in drug discovery253Machine learning in energy storage materials106Augmented Reality, a Review of a Way to Represent and Manipulate 3D Chemical Structures76Recent advances and applications of deep learning methods in materials science1161Machine learning prediction and experimental verification of Pt-modified nitride catalysts for ethanol reforming with reduced precious metal loading29Opportunities and challenges in applying machine learning to voltammetric mechanistic studies29ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: The progression of data analytics and artificial intelligence in support of sustainable development in animal science42Machine Learning Applications for Chemical Reactions46Modeling Operando Electrochemical CO 2 Reduction155Evaluation guidelines for machine learning tools in the chemical sciences180Interpretable and Explainable Machine Learning for Materials Science and Chemistry421Comparative Evaluation of Light‐Driven Catalysis: A Framework for Standardized Reporting of Data**75Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy70Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review50Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors294Machine Learning of Coupled Cluster (T)-Energy Corrections via Delta (Δ)-Learning52Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges292Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory35Predicting Adhesive Free Energies of Polymer–Surface Interactions with Machine Learning45Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137When not to use machine learning: A perspective on potential and limitations51Selective functionalization of hindered meta-C–H bond of o-alkylaryl ketones promoted by automation and deep learning31Chemistry-Informed Machine Learning Enables Discovery of DNA-Stabilized Silver Nanoclusters with Near-Infrared Fluorescence83Prediction of wear performance of ZK60 / CeO2 composites using machine learning models86Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery55Machine learning for battery research110Into the Unknown: How Computation Can Help Explore Uncharted Material Space65Water electrolysis498Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Machine learning modeling practices to support the principles of AI and ethics in nutrition research47Neural network potentials for chemistry: concepts, applications and prospects128Data-driven design of electrocatalysts: principle, progress, and perspective46Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry52Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant49Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges88Open data and algorithms for open science in AI-driven molecular informatics27Machine Learning Interatomic Potentials and Long-Range Physics223Bridging the complexity gap in computational heterogeneous catalysis with machine learning280How to validate machine-learned interatomic potentials130An Ecosystem for Digital Reticular Chemistry52Uncertainty-aware predictions of molecular x-ray absorption spectra using neural network ensembles27Perovskite oxide composites for bifunctional oxygen electrocatalytic activity and zinc-air battery application- a mini-review81Generative Models as an Emerging Paradigm in the Chemical Sciences313Application of machine learning to guide efficient metal leaching from spent lithium-ion batteries and comprehensively reveal the process parameter influences59Transfer Learning Facilitates the Prediction of Polymer–Surface Adhesion Strength26Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives241When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry65Accelerating the Discovery of g-C3N4-Supported Single Atom Catalysts for Hydrogen Evolution Reaction: A Combined DFT and Machine Learning Strategy79Designing materials acceleration platforms for heterogeneous CO2 photo(thermal)catalysis28The “Collections as ML Data” checklist for machine learning and cultural heritage26Deep learning criminal networks43Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network78Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use37Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation85A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis71Functional Material Systems Enabled by Automated Data Extraction and Machine Learning33Fluorescent Dissolved Organic Matter Components as Surrogates for Disinfection Byproduct Formation in Drinking Water: A Critical Review56How to efficient and high-value recycling of electronic components mounted on waste printed circuit boards: Recent progress, challenge, and future perspectives69A review of machine learning methods applied to structural dynamics and vibroacoustic149Machine Learning Methods for Small Data Challenges in Molecular Science499Tribological properties study and prediction of PTFE composites based on experiments and machine learning46Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning35Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing75Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven Classifier32Recent advances and applications of machine learning in electrocatalysis36do-mpc: Towards FAIR nonlinear and robust model predictive control126Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76Emerging Two-Dimensional Organic Semiconductor-Incorporated Perovskites─A Fascinating Family of Hybrid Electronic Materials101Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis33Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction207Low-dimensional neural ODEs and their application in pharmacokinetics34Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions26Application of Digital Methods in Polymer Science and Engineering28Potential for Machine Learning to Address Data Gaps in Human Toxicity and Ecotoxicity Characterization50Is AI leading to a reproducibility crisis in science?1062D Materials in Flexible Electronics: Recent Advances and Future Prospectives363Accelerated chemical science with AI86Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications86Towards physics-informed explainable machine learning and causal models for materials research34Application of machine learning in chemical engineering: outlook and perspectives31The emergence of machine learning force fields in drug design30Predictive Minisci late stage functionalization with transfer learning29Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks39Predicting Lewis Acidity: Machine Learning the Fluoride Ion Affinity of p‐Block‐Atom‐Based Molecules35Machine learning accelerates the screening of single-atom catalysts towards CO2 electroreduction30The Future of Material Scientists in an Age of Artificial Intelligence76Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices30Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors63Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants44Designer Anions for Better Rechargeable Lithium Batteries and Beyond50Evolution of artificial intelligence as a modern technology in advanced cancer therapy30Recent advances and applications of deep learning, electroencephalography, and modern analysis techniques in screening, evaluation, and mechanistic analysis of taste peptides47eutXG: A Machine-Learning Model to Understand and Predict the Melting Point of Novel X-Bonded Deep Eutectic Solvents35Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends56Do machine learning methods improve prediction of ambient air pollutants with high spatial contrast? A systematic review36ROBERT : Bridging the Gap Between Machine Learning and Chemistry49Machine learning for the advancement of membrane science and technology: A critical review107Accelerating the Discovery of Efficient High-Entropy Alloy Electrocatalysts: High-Throughput Experimentation and Data-Driven Strategies67Machine learning in materials research: Developments over the last decade and challenges for the future105Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment44Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations109Recent progress on dynamics and control of pipes conveying fluid106A hybrid modelling approach to compare chemical separation technologies in terms of energy consumption and carbon dioxide emissions65Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems30Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 202328Integrating artificial intelligence in energy transition: A comprehensive review248Machine learning-assisted carbon dots synthesis and analysis: State of the art and future directions27Recent advances in Fe-based metal-organic frameworks: Structural features, synthetic strategies and applications79Reproducibility in machine‐learning‐based research: Overview, barriers, and drivers85Benchmarking machine learning models for predicting lithium ion migration27Machine learning-enabled optoelectronic material discovery: a comprehensive review31A general framework to govern machine learning oriented materials data quality42Artificial Intelligence‐Driven Approaches in Semiconductor Research44Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research54Developing machine learning for heterogeneous catalysis with experimental and computational data93Artificial intelligence for drug delivery: Yesterday, today and tomorrow27PET-MAD as a lightweight universal interatomic potential for advanced materials modeling57

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  3. Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

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