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RESEARCH PAPER · Science Advances · 2018

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

Fang Ren, Logan T. Ward, Travis Williams, Kevin James Laws, Christopher Wolverton, Jason R. Hattrick-Simpers, Apurva Mehta

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Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experimentsOriginalCan machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery264Concrete material science: Past, present, and future innovations398Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing340A machine learning approach for engineering bulk metallic glass alloys246Predicting glass transition temperatures using neural networks179Synchrotron Big Data Science74Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry270Machine learning of optical properties of materials – predicting spectra from images and images from spectra132Recent Advances in Metallic Glass Nanostructures: Synthesis Strategies and Electrocatalytic Applications84A quantitative uncertainty metric controls error in neural network-driven chemical discovery240Nanoinformatics, and the big challenges for the science of small things92Phase selection motifs in High Entropy Alloys revealed through combinatorial methods: Large atomic size difference favors BCC over FCC270Materials informatics: From the atomic-level to the continuum191Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design832Development of advanced materials via entropy engineering103From DFT to machine learning: recent approaches to materials science–a review849Machine learning for data-driven discovery in solid Earth geoscience1249Functional Applications of Metallic Glasses in Electrocatalysis77Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures48Progress toward autonomous experimental systems for alloy development46A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules113Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets70Machine learning guided design of functional materials with targeted properties60Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses96Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks325Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis333Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data129Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management73Ultra-high temperature ceramics melting temperature prediction via machine learning79Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential50Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning116Artificial intelligence for materials discovery120Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics185A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering104Data‐Driven Materials Science: Status, Challenges, and Perspectives700Accelerated Search for BaTiO3‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design100Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films127Prediction and optimization of epoxy adhesive strength from a small dataset through active learning102Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning65Unsupervised discovery of solid-state lithium ion conductors312A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys285A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses96Model-driven design of bioactive glasses: from molecular dynamics through machine learning62Enabling Catalyst Discovery through Machine Learning and High-Throughput Experimentation133Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules124Autonomous intelligent agents for accelerated materials discovery95Bayesian-optimization-guided experimental search of NASICON-type solid electrolytes for all-solid-state Li-ion batteries72Deep learning aided rational design of oxide glasses102High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery179A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns199Accelerated design of novel W-free high-strength Co-base superalloys with extremely wide γ/γʹ region by machine learning and CALPHAD methods98The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research78Crystal symmetry determination in electron diffraction using machine learning168Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures113Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics46Decisive Structural and Functional Characterization of Halide Perovskites with Synchrotron49Combinatorial study of thermal stability in ternary nanocrystalline alloys66Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99Machine Learning for Structural Materials61Discovery of high-entropy ceramics via machine learning276Machine learning in materials genome initiative: A review231Opportunities and Challenges for Machine Learning in Materials Science523Self-driving laboratory for accelerated discovery of thin-film 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Titanium Alloys with Low Elastic Modulus as Implant Materials110AI Applications through the Whole Life Cycle of Material Discovery192Searching for high entropy alloys: A machine learning approach215Machine learning in experimental materials chemistry66Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach52Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set74Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm387Machine learning-based glass formation prediction in multicomponent alloys133Chemical Robotics Enabled Exploration of Stability in Multicomponent Lead Halide Perovskites via Machine Learning130Machine learning-assisted discovery of strong and conductive Cu alloys: Data mining from discarded experiments and physical features86Machine learning-guided synthesis of advanced inorganic materials157Emerging materials intelligence ecosystems propelled by machine learning441Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm246Physics-informed machine learning for composition – process – property design: Shape memory alloy demonstration131Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses110Predicting materials properties without crystal structure: deep representation learning from stoichiometry400Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders119Artificial intelligence and machine learning in design of mechanical materials690A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds58Accelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach73Toward autonomous design and synthesis of novel inorganic materials159Digital Transformation in Materials Science: A Paradigm Change in Material's Development72A critical review: Recent advances in “digital” biomolecule detection with single copy sensitivity68Compositional grading of a 316L-Cu multi-material part using machine learning for the determination of selective laser melting process parameters105Machine learning in polymer informatics200Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity89A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing132Machine learning prediction of glass-forming ability in bulk metallic glasses58Using simulation to accelerate autonomous experimentation: A case study using mechanics67Machine learning of phases and mechanical properties in complex concentrated alloys162Machine learning identified materials descriptors for ferroelectricity110A focused review on machine learning aided high-throughput methods in high entropy alloy134Development of ultrahigh-entropy ceramics with tailored oxidation behavior72A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys47Materials representation and transfer learning for multi-property prediction71Accelerated discovery of high-performance Cu-Ni-Co-Si alloys through machine learning84Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications54G ryffin : An algorithm for Bayesian optimization of categorical variables informed by expert knowledge170Machine Learning to Predict Quasicrystals from Chemical Compositions62Machine learning for alloys698Autonomous experimentation systems for materials development: A community perspective398Machine Learning Roadmap for Perovskite Photovoltaics85Structural and chemical evolution in layered oxide cathodes of lithium-ion batteries revealed by synchrotron techniques72Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning50High-throughput rapid experimental alloy development (HT-READ)94Data-driven discovery of a universal indicator for metallic glass forming ability158Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains225Closed-Loop Electrolyte Design for Lithium-Mediated Ammonia Synthesis69Machine-learning and high-throughput studies for high-entropy materials165Toward autonomous materials research: Recent progress and future challenges61Progress and Perspective of Metallic Glasses for Energy Conversion and Storage71Roadmap on Machine learning in electronic structure180Data-driven glass-forming ability criterion for bulk amorphous metals with data augmentation54Machine learning in energy storage materials106Accelerated discovery of high-performance piezocatalyst in BaTiO3-based ceramics via machine learning84Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction55Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities56Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery73Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare637Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions101Design of super-hard high-entropy ceramics coatings via machine learning62Bayesian optimization with experimental failure for high-throughput materials growth53Machine learning versus human learning in predicting glass-forming ability of metallic glasses56Machine learning accelerates the materials discovery142Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery148Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics137A critical examination of robustness and generalizability of machine learning prediction of materials properties146Managing innovation in the era of AI138Application of machine learning to guide efficient metal leaching from spent lithium-ion batteries and comprehensively reveal the process parameter influences59Methods, progresses, and opportunities of materials informatics72Recent applications of machine learning in alloy design: A review158High-throughput and data-driven machine learning techniques for discovering high-entropy alloys77Advanced Computational Methods for Modeling, Prediction and Optimization—A Review132A prompt-engineered large language model, deep learning workflow for materials classification50Data‐Driven Materials Research and Development for Functional Coatings120New opportunity: Materials genome strategy for engineered cementitious composites (ECC) design70Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials68Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry105

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