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RESEARCH PAPER · Physical Review Materials · 2018

Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds

Kyoungdoc Kim, Logan T. Ward, Jiangang He, Amar Krishna, Ankit Agrawal, Chris M. Wolverton

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Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compoundsOriginalDouble Half-Heuslers187From DFT to machine learning: recent approaches to materials science–a review848A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction282Determining the Optimal Phase-Change Material via High-Throughput Calculations4Recent advances and applications of machine learning in solid-state materials science2604Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation337Application of fuzzy learning in the research of binary alloys: Revisit and validation7Exploring Two-Dimensional Materials Thermodynamic Stability via Machine Learning141Double perovskites with 3d and 4d/5d transition metals: compounds with promises132High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery179Machine-enabled inverse design of inorganic solid materials: promises and challenges190Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks142Searching high spin polarization ferromagnet in Heusler alloy via machine learning19Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions70Discovering Intermetallics Through Synthesis, Computation, and Data‐Driven Analysis29Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine Learning51Opportunities and Challenges for Machine Learning in Materials Science522DID Code: A Bridge Connecting the Materials Genome Engineering Database with Inheritable Integrated Intelligent Manufacturing11Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery281Targeting Productive Composition Space through Machine-Learning-Directed Inorganic Synthesis36Experimental inspection of a computationally-designed NiCrMnSi Heusler alloy with high Curie temperature5Machine learning enabled discovery of application dependent design principles for two-dimensional materials18Magnetic Full-Heusler Compounds for Thermoelectric Applications16Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation44Machine learning formation enthalpies of intermetallics22Explainable machine learning for materials discovery: predicting the potentially formable Nd–Fe–B crystal structures and extracting the structure–stability relationship14High-throughput design of magnetic materials52Structural and magnetic properties of epitaxial films of CoIrMnAl equiatomic quaternary Heusler alloy designed from first-principles calculation0High-throughput design of magnetic materials3Review of high-throughput computational design of Heusler alloys84Topological representations of crystalline compounds for the machine-learning prediction of materials properties112Structural and magnetic properties of CoIrMnAl equiatomic quaternary Heusler alloy epitaxial films designed using first-principles calculations8Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts105Structural, Electronic, Magnetic, and Elastic Properties of CoX′CrZ (X′ = Sc, Ti; Z = Al, Ga) Quaternary Heusler Alloys: First‐Principles Study7Machine learnings for CVD graphene analysis: From measurement to simulation of SEM images14Chembot: A Machine Learning Approach to Selective Configuration Interaction31Machine Learning in Solid‐State Chemistry: Heusler Compounds0Machine learning for materials discovery: Two-dimensional topological insulators71High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks136Synthesizable Double Perovskite Oxide Search via Machine Learning and High‐Throughput Computational Screening18Machine Learning Accelerated, High Throughput, Multi‐Objective Optimization of Multiprincipal Element Alloys37Predicting energy and stability of known and hypothetical crystals using graph neural network43Deformation potential extraction and computationally efficient mobility calculations in silicon from first principles67Predicting synthesizability of crystalline materials via deep learning43Crystal graph attention networks for the prediction of stable materials153High-Throughput Computational Discovery of Ternary Mixed-Anion Oxypnictides20Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products29Computational design of polymer nanocomposites1Recent progress in computational discovery of Heusler alloys10Roadmap on Machine learning in electronic structure180Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction90Extensible Structure-Informed Prediction of Formation Energy with improved accuracy and usability employing neural networks36Scale-invariant machine-learning model accelerates the discovery of quaternary chalcogenides with ultralow lattice thermal conductivity39Deep dive into machine learning density functional theory for materials science and chemistry109Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain12Computationally accelerated discovery of functional and structural Heusler materials14Toward machine learning for microscopic mechanisms: A formula search for crystal structure stability based on atomic properties7True colours shining through: Determining site distributions in coloured Li-containing quaternary Heusler compounds3Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery551Rapid discovery of stable materials by coordinate-free coarse graining62Heusler alloys: Past, properties, new alloys, and prospects267A machine learning approach to predict the structural and magnetic properties of Heusler alloy families24Machine learned synthesizability predictions aided by density functional theory30Quantitative high-throughput measurement of bulk mechanical properties using commonly available equipment11Prediction of mechanical properties for deep drawing steel by deep learning22Machine learning enabled rational design of atomic catalysts for electrochemical reactions19Probabilistic selection and design of concrete using machine learning24Research Network Analysis and Machine Learning on Heusler Alloys14Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques2Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials109Screening Platform for Promising Na Superionic Conductors for Na-Ion Solid-State Electrolytes23Accelerating the Search for Stable Full Heusler Compounds through Machine Learning1Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques21An Experimental High-Throughput to High-Fidelity Study Towards Discovering Al–Cr Containing Corrosion-Resistant Compositionally Complex Alloys27Screening New Quaternary Semiconductor Heusler Compounds By Machine-Learning Methods8Screening Mixed-Metal Sn2M(III)Ch2X3 Chalcohalides for Photovoltaic Applications17Machine-learning surrogate model for accelerating the search of stable ternary alloys5Accelerating the prediction of stable materials with machine learning104High-aspect-ratio nanostructured hydroxyapatite: towards new functionalities for a classical material79Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams13Remarkable Optoelectronic Characteristics of Synthesizable Square‐Octagon Haeckelite Structures: Machine Learning Materials Discovery22A machine learning‐based crystal graph network and its application in development of functional materials21Explorative prediction of novel superhard carbon allotropes with lager cell: Density functional theory-assisted deep learning10Prospects for the database development in electrical engineering materials1Emerging ZnO Semiconductors for Photocatalytic CO2 Reduction to Methanol50Phase Prediction via Crystal Structure Similarity in the Periodic Number Representation3Construction and application of materials database under data-driven research paradigm3Predicting elastic and electronic properties of quaternary Heusler alloys using machine learning and DFT calculations8Combination of Transfer Learning and Chemprop Interpreter with Support of Deep Learning for the Energy Levels of Organic Photovoltaic Materials Prediction and Regulation11Many-body expansion based machine learning models for octahedral transition metal complexes2Optimizing CO 2 electroreduction: theoretical insights for enhancing efficiency across elementary steps65Predicting concrete strength using machine learning and fresh-state measurements2Machine learning assisted development of Heusler alloys for high magnetic moment6Crystal Structure Prediction of Cs–Te with Supervised Machine Learning2Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion27Application of machine learning in magnetocaloric materials: A review12Recent progress in the design and application of machine learning for the hydrogen evolution reaction in electrocatalysis and photocatalysis3Discovery and design of Equiatomic Heusler alloys magnetic properties using machine learning with DFT approach3Computational Material Science Has a Data Problem4Theoretical prediction of electronic and thermal transport properties of the double half-Heusler compounds X 2 FeNiSn 2 ( X = Ta , Nb , V )6Explainable Attention CNN for Predicting Properties of Heusler Alloys1Machine learning investigation of chemical disorder effects in 24-valence-electron full-Heusler alloys with single magnetic element3Machine learning for the development of new materials for a magnetic tunnel junction11Energy-GNoME: A living database of selected materials for energy applications5Self-learning quantum Monte Carlo: Application to the classical Holstein spin-fermion model1Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules29Formation energy prediction of material crystal structures using deep learning1Artificial intelligence transforming materials research: possible road ahead for India1LEAD: Literature Enhanced Ab Initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions0Strategies to enhance the supercapacitance performance of 2D MXenes through defect engineering, doping, hybridization, and magnetic field assistance: recent progress and challenges10Machine learning technique for prediction of crystal structure in austenitic and martensitic phase of Ni–Mn-based Heusler alloys1Accurate screening of functional materials with machine-learning potential and transfer-learned regressions: Heusler alloy benchmark2PNcsp+: A Periodic Number-Based Crystal Structure Prediction Method Enhanced by Machine Learning2Accelerating the discovery of athermal deep-ultraviolet nonlinear optical crystals via physics-informed herarchical graph learning1ABC2-type short-wave infrared photodetector materials discovered via high-throughput screening and machine learning0A Comparative Study of Structural Representations for 2D Materials: Insights from Dynamic Collision Fingerprint and Matminer Library0Autonomous Materials Exploration Integrates Automated Phase Identification and AI Agents Enhanced by Human Guidance0

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