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RESEARCH PAPER · Nature · 2019

Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis

Xiwen Jia, Allyson Lynch, Yuheng Huang, Matthew Danielson, Immaculate Lang’at, Alexander Milder, Aaron Ruby, Hao Wang, Sorelle A. Friedler, Alexander J. Norquist, Joshua Schrier

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Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesisOriginalProgress and prospects for accelerating materials science with automated and autonomous workflows228‘Ring Breaker’: Assessing Synthetic Accessibility of the Ring System Chemical Space0‘Ring Breaker’: Assessing Synthetic Accessibility of the Ring System Chemical Space0Notizen aus der Chemie0AI-assisted synthesis prediction55The good, the bad, and the ugly in chemical and biological data for machine learning54High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane179Machine learning the ropes: principles, applications and directions in synthetic chemistry279Predicting the Effect of Chemical Factors on the pH of Crystallisation Trials0Selective metathesis synthesis of MgCr 2 S 4 by control of thermodynamic driving forces44A Critical Review of Machine Learning of Energy Materials606A Crystallization Robot for Generating True Random Numbers Based on Stochastic Chemical Processes22Successes and Opportunities for Discovery of Metal Oxide Photoanodes for Solar Fuels Generators36Synthetically Accessible Virtual Inventory (SAVI)12Synthetically Accessible Virtual Inventory (SAVI)6“Ring Breaker”: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space40Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?12Predicting the Effect of Chemical Factors on the pH of Crystallization Trials7Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models7Twenty years of exceptional success: The molecular education and research consortium in undergraduate computational chemistry ( MERCURY )29Robot-Accelerated Perovskite Investigation and Discovery187Watch the colors: or about qualitative thinking in chemistry5Scientific AI in materials science: a path to a sustainable and scalable paradigm119Big-Data Science in Porous Materials: Materials Genomics and Machine Learning5982020 roadmap on solid-state batteries141Understanding the diversity of the metal-organic framework ecosystem616Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature85Machine-learned metrics for predicting the likelihood of success in materials discovery38Molecular Machine Learning: The Future of Synthetic Chemistry?72Molekulares maschinelles Lernen: Die Zukunft der Synthesechemie?3Sequential pairwise reactions dictate phase evolution in the solid-state synthesis of multicomponent ceramics0Adaptive Optimization of Chemical Reactions with Minimal Experimental Information82Artificial intelligence and automation in computer aided synthesis planning89The Role of Machine Learning in the Understanding and Design of Materials434SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules66COVID-19 research risks ignoring important host genes due to pre-established research patterns30Mechanistic insight of KBiQ 2 (Q = S, Se) using panoramic synthesis towards synthesis-by-design27Optimization of the Bulk Heterojunction of All-Small-Molecule Organic Photovoltaics Using Design of Experiment and Machine Learning Approaches42Small Data Machine Learning: Classification and Prediction of Poly(ethylene terephthalate) Stabilizers Using Molecular Descriptors32Protein storytelling through physics80Data-driven materials research enabled by natural language processing and information extraction302Artificial Intelligence: Unpacking Political, Rhetorical, and Security Factors1Future Possibilities0In silico protein science: from sequence to network1Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles40Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of Methane54Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design55Opportunities and challenges of text mining in materials research180Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds2Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias114Automation and computer-assisted planning for chemical synthesis191Predicting Reaction Yields via Supervised Learning187Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion19What's Left for a Computational Chemist To Do in the Age of Machine Learning?10Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches72Atomic understanding of structural deformations upon ablation of graphene10Observing and Modeling the Sequential Pairwise Reactions that Drive Solid‐State Ceramic Synthesis89Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery55Predicting inorganic dimensionality in templated metal oxides8Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane23Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts77Best practices in machine learning for chemistry504Evaluating the Performance of a Transformer-based Organic Reaction Prediction Model4Scalable HPC & AI infrastructure for COVID-19 therapeutics24Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Using automated serendipity to discover how trace water promotes and inhibits lead halide perovskite crystal formation24Design and Application of a Screening Set for Monophosphine Lig-ands in Metal Catalysis7Highly accurate prediction of reaction performance and enantioselectivity with a uniform machine learning protocol0Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery0Panoramic (in beam) studies of materials synthesis1Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks197Approaches using AI in medicinal chemistry2Mimicking nature to control bio-material surface wetting and adhesion109Ultrahigh Throughput Protein–Ligand Docking with Deep Learning7Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature2Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries65Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery97Shapley Additive Explanations of Multigeometrical Variable Coupling Effect in Transonic Compressor22Towards Predictive Synthesis of Inorganic Materials Using Network Science5Knowledge extraction in catalysis utilizing design of experiments and machine learning29Artificial Intelligence based Approach for Rapid Material Discovery: From Chemical Synthesis to Quantum Materials0Predicting compositional changes of organic–inorganic hybrid materials with Augmented CycleGAN4Active meta-learning for predicting and selecting perovskite crystallization experiments24Innovative Separations Research and Development Needs for Advanced Fuel Cycles9A review of the recent progress in battery informatics178Autonomous design of new chemical reactions using a variational autoencoder27Machine Learning for the Discovery, Design, and Engineering of Materials66The case for data science in experimental chemistry: examples and recommendations83Modeling antiphase boundary energies of Ni3Al-based alloys using automated density functional theory and machine learning67Machine Learning for Chemical Reactivity: The Importance of Failed Experiments218Maschinelles Lernen zur Vorhersage chemischer Reaktivität: Die Bedeutung “gescheiterter” Experimente8Chemical property prediction under experimental biases5Theory‐Guided Machine Learning to Predict the Performance of Noble Metal Catalysts in the Water‐Gas Shift Reaction11Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature83Advancing Base Metal Catalysis through Data Science: Insight and Predictive Models for Ni-Catalyzed Borylation through Supervised Machine Learning24Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels10Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling57Mechanistic Inference from Statistical Models at Different Data-Size Regimes37Reaction impurity prediction using a data mining approach3Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds33Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models7Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models5Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Principled Exploration of Bipyridine and Terpyridine Additives to Promote Methylammonium Lead Iodide Perovskite Crystallization13Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Why big data and compute are not necessarily the path to big materials science51Artificial intelligence in computational materials science8Training data selection for accuracy and transferability of interatomic potentials56Machine learning accelerates the materials discovery142Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118High-Throughput Experimentation for Resource-Efficient Discovery of Methane Functionalization Catalysts1Predictive stochastic analysis of massive filter-based electrochemical reaction networks31A catalyst acceleration platform toward realizing the energy transition2Improving molecular machine learning through adaptive subsampling with active learning24Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane15Pharmacophore-based virtual screening of large compound databases can aid “big data” problems in drug discovery7Retrosynthesis from transforms to predictive sustainable chemistry and nanotechnology: a brief tutorial review19Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis1The rise of automated curiosity-driven discoveries in chemistry20Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics137Porous Molecular Materials1Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction41Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-Generated Protein–Ligand Structures: Towards Per-Target Scoring Functions17Closed-loop optimization of nanoparticle synthesis enabled by robotics and machine learning72The value of negative results in data-driven catalysis research72Reaction Impurity Prediction using a Data Mining Approach**9A critical examination of robustness and generalizability of machine learning prediction of materials properties145ET-AL: Entropy-targeted active learning for bias mitigation in materials data30Introduction to artificial intelligence and deep learning using interactive electronic programming notebooks5Combatting over-specialization bias in growing chemical databases5Auto-MatRegressor: liberating machine learning alchemists63ChemoGraph: Interactive Visual Exploration of the Chemical Space8Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature65Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit39Artificial Intelligence in Evaluation of Permanent Impairment: New Operational Frontiers7Unified roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and their Impact on Particle Size and Morphology2Machine Learning for the Optimization of Chemical Reaction Conditions6Data-Driven Imputation of Miscibility of Aqueous Solutions via Graph-Regularized Logistic Matrix Factorization6In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials41VOLTA: a tool for battery screening bridging the gap between virtual electrode materials and practical applications0Advanced Design of a III‐Nitride Light‐Emitting Diode via Machine Learning11Exploiting redundancy in large materials datasets for efficient machine learning with less data118Accelerated chemical science with AI86Artificial intelligence-powered electronic skin254Accelerating materials research with a comprehensive data management tool: a case study on an electrochemical laboratory13Computational chemistry for water-splitting electrocatalysis238Learning the Next Experiment: A Decision Architecture for Closed-Loop Drug Discovery under Cost, Uncertainty, and Scientific Value0Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts26Paving the road towards automated homogeneous catalyst design32

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