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RESEARCH PAPER · Nature · 2016

Machine-learning-assisted materials discovery using failed experiments

Paul Raccuglia, Katherine C. Elbert, Philip Adler, Casey Falk, Malia B. Wenny, Aurelio Mollo, Mat­thias Zeller, Sorelle A. Friedler, Joshua Schrier, Alexander J. Norquist

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Machine-learning-assisted materials discovery using failed experimentsOriginalBig data need big theory too226Recent Progress in Ionic Iridium(III) Complexes for Organic Electronic Devices273Auditing Black-Box Models for Indirect Influence268Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials442An updated roadmap for the integration of metal–organic frameworks with electronic devices and chemical sensors1210An informatics approach to transformation temperatures of NiTi-based shape memory alloys272Deep learning for computational chemistry799Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage252A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality573Hyperspectral Imaging for Presymptomatic Detection of Tobacco Disease with Successive Projections Algorithm and Machine-learning Classifiers214Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability202Materials discovery and design using machine learning1266Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future233Application of Artificial Neural Networks for Catalysis: A Review228Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning471Quantum Machine Learning in Chemical Compound Space246Optimizing Chemical Reactions with Deep Reinforcement Learning438Material structure-property linkages using three-dimensional convolutional neural networks336Machine learning meets volcano plots: computational discovery of cross-coupling catalysts209Towards operando computational modeling in heterogeneous catalysis250Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning395Predicting reaction performance in C–N cross-coupling using machine learning1051Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.1707Inverse design in search of materials with target functionalities513An open experimental database for exploring inorganic materials202Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments524Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning277Accelerating the discovery of materials for clean energy in the era of smart automation776Machine Learning in Computer-Aided Synthesis Planning706A strategy to apply machine learning to small datasets in materials science776Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons201Combining theory and experiment in lithium–sulfur batteries: Current progress and future perspectives446Less is more: Sampling chemical space with active learning802Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene396Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale302Crystal Structure Prediction via Deep Learning355Learning atoms for materials discovery200Controlling an organic synthesis robot with machine learning to search for new reactivity739Machine learning for molecular and materials science4890High-throughput discovery of organic cages and catenanes using computational screening fused with robotic synthesis201How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics316Interface Characterization and Control of 2D Materials and Heterostructures212Inverse molecular design using machine learning: Generative models for matter engineering2049The 2019 materials by design roadmap322Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering346Metrology for the next generation of semiconductor devices500ElemNet: Deep Learning the Chemistry of Materials 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design and efficiency prediction for high-performance organic photovoltaic materials343Uncovering the eutectics design by machine learning in the Al–Co–Cr–Fe–Ni high entropy system275Designing phononic crystal with anticipated band gap through a deep learning based data-driven method256Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning339Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models461Machine Learning for Catalysis Informatics: Recent Applications and Prospects612Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening310Energy-efficient separation alternatives: metal–organic frameworks and membranes for hydrocarbon separation746Integrating Physics-Based Modeling with Machine Learning: A Survey285Machine learning the ropes: principles, applications and directions in synthetic chemistry279Machine learning-driven new material discovery296Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation618Hydrogel machines1020New Concepts in Electrolytes943Machine learning: Accelerating materials development for energy storage and conversion370A Structure-Based Platform for Predicting Chemical Reactivity288Deep-learning-enabled self-adaptive microwave cloak without human intervention583Trends in 3D Printing Processes for Biomedical Field: Opportunities and Challenges210The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules282Machine learning in materials genome initiative: A review231Opportunities and Challenges for Machine Learning in Materials Science522Nanostructural metallic materials: Structures and mechanical properties326Mechanical properties and deformation mechanisms of gradient nanostructured metals and alloys846Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation659On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations203AI Applications through the Whole Life Cycle of Material Discovery192Applications of machine learning in metal-organic frameworks225Novel Heterogeneous Catalysts for CO 2 Hydrogenation to Liquid Fuels361Invited review: Machine learning for materials developments in metals additive manufacturing231The Role of Machine Learning in the Understanding and Design of Materials434Artificial intelligence and machine learning in design of mechanical materials689Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys259Inverse design of nanoporous crystalline reticular materials with deep generative models399Machine learning for advanced energy materials208Machine learning for perovskite materials design and discovery536Prediction of chemical reaction yields using deep learning291Machine learning in materials science: From explainable predictions to autonomous design280Automation and computer-assisted planning for chemical synthesis191Artificial intelligence in drug discovery: recent advances and future perspectives457Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation239Best practices in machine learning for chemistry504Accelerated and conventional development of magnetic high entropy alloys207Perspective on integrating machine learning into computational chemistry and materials science203Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems842Nanoparticle synthesis assisted by machine learning544Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Artificial Intelligence in Chemistry: Current Trends and Future Directions286Autonomous experimentation systems for materials development: A community perspective398Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats323Ab Initio Machine Learning in Chemical Compound Space195Machine Learning: New Ideas and Tools in Environmental Science and Engineering945Emerging applications of zeolites in catalysis, separation and host–guest assembly585Artificial Intelligence Applied to Battery Research: Hype or Reality?503The latest process and challenges of microwave dielectric ceramics based on pseudo phase diagrams211Innovative Materials Science via Machine Learning194Error Metrics and Performance Fitness Indicators for Artificial Intelligence and Machine Learning in Engineering and Sciences464An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles365Recent Progress and Future Prospects on All-Organic Polymer Dielectrics for Energy Storage Capacitors744A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+235MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**237Review of computational approaches to predict the thermodynamic stability of inorganic solids196Tackling Climate Change with Machine Learning1012Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems877EIS equivalent circuit model prediction using interpretable machine learning and parameter identification using global optimization algorithms241Prediction and Design of Nanozymes using Explainable Machine Learning192Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors294Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Machine learning for high-entropy alloys: Progress, challenges and opportunities397Explainable machine learning in materials science500The reformation of catalyst: From a trial-and-error synthesis to rational design299Scope of machine learning in materials research—A review230Exploring the Specificity of Nanozymes265Battery safety: Machine learning-based prognostics210AI energized hydrogel design, optimization and application in biomedicine193Machine Learning Aided Design and Optimization of Thermal Metamaterials194A review on copper alloys with high strength and high electrical conductivity230Self-Driving Laboratories for Chemistry and Materials Science664Accelerated data-driven materials science with the Materials Project299

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