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RESEARCH PAPER · Physical review. B./Physical review. B · 2016

Predicting density functional theory total energies and enthalpies of formation of metal-nonmetal compounds by linear regression

Ann M. Deml, Ryan O’Hayre, Chris M. Wolverton, Vladan Stevanović

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Predicting density functional theory total energies and enthalpies of formation of metal-nonmetal compounds by linear regressionOriginalAtomistic calculations and materials informatics: A review238A general-purpose machine learning framework for predicting properties of inorganic materials1789ChemInform Abstract: Predicting Density Functional Theory Total Energies and Enthalpies of Formation of Metal—Nonmetal Compounds by Linear Regression5A computational framework for automation of point defect calculations210ESIF 2016: Modernizing Our Grid and Energy System1Representation of compounds for machine-learning prediction of physical properties304Formation enthalpies for transition metal alloys using machine learning36Multi-method identification and characterization of the intermetallic surface layers of hot-dip Al-coated steel: FeAl 3 or Fe 4 Al 13 and Fe 2 Al 5 or Fe 2 Al 5+x62Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations410Machine Learning and Materials Informatics: Recent Applications and Prospects8Ab Initio Study of Stability of Na2Fe2(SO4)3, a High Potential Na-Ion Battery Cathode Material33Machine learning in materials informatics: recent applications and prospects1912Machine learning properties of binary wurtzite superlattices31Exploring a potential energy surface by machine learning for characterizing atomic transport36Matminer: An open source toolkit for materials data mining1068Database‐Driven Materials Selection for Semiconductor Heterojunction Design29A machine learning approach for engineering bulk metallic glass alloys246Data-centric science for materials innovation42ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition509Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction20Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries199Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential50Exploring Two-Dimensional Materials Thermodynamic Stability via Machine Learning141Reliable and explainable machine-learning methods for accelerated material discovery211Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning339Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning97Gapped metals as thermoelectric materials revealed by high-throughput screening31In silico investigation of Cu(In,Ga)Se 2 -based solar cells3Fundamental band gap and alignment of two-dimensional semiconductors explored by machine learning*33Guided patchwork kriging to develop highly transferable thermal conductivity prediction models22An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts135Experimental, computational and thermodynamic studies in perovskites metal oxides for thermochemical fuel production: A review94Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices508Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery281Targeting Productive Composition Space through Machine-Learning-Directed Inorganic Synthesis36A critical examination of compound stability predictions from machine-learned formation energies268Spinel-Type nitride compounds with improved features as solar cell absorbers17Orbital graph convolutional neural network for material property prediction153Determination of Formation Energies and Phase Diagrams of Transition Metal Oxides with DFT+U41Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the $σ-$phase as an example0A critical examination of compound stability predictions from machine-learned formation energies194Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm246Factors controlling oxophilicity and carbophilicity of transition metals and main group metals69The effects of the chemical composition on the structural, thermodynamic, and mechanical properties of all-inorganic halide perovskites23Machine learning in materials science: From explainable predictions to autonomous design280Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds17Development, characterization, and modeling of a high-performance Ru/B2CA catalyst for ammonia synthesis17Catalyze Materials Science with Machine Learning66Statistical analysis for comparing and predicting rutting resistance of asphalt pavements with rigid and flexible geogrid layers22Supervised deep learning prediction of the formation enthalpy of complex phases using a DFT database: The σ− phase as an example19Predicting energy and stability of known and hypothetical crystals using graph neural network43Capacity prediction of K-ion batteries: a machine learning based approach for high throughput screening of electrode materials30Thermodynamic stability of Pd–Ru alloy nanoparticles: combination of density functional theory calculations, supervised learning, and Wang–Landau sampling12Hubbard-corrected oxide formation enthalpies without adjustable parameters10Prediction of the Electron Density of States for Crystalline Compounds with Atomistic Line Graph Neural Networks (ALIGNN)39Methodological framework for materials discovery using machine learning9Moving closer to experimental level materials property prediction using AI35Discovery of New Plasmonic Metals via High‐Throughput Machine Learning8Bandgap prediction on small thermoelectric material dataset via instance-based transfer learning10Hierarchical machine learning based structure–property correlations for as–cast complex concentrated alloys16Identification of potential solid-state Li-ion conductors with semi-supervised learning44Machine learning and density functional theory simulation of the electronic structural properties for novel quaternary semiconductors14Learning from mistakes: Sampling strategies to efficiently train machine learning models for material property prediction21Bayesian approach for inferrable machine learning models of process–structure–property linkages in complex concentrated alloys25Gradient boosted and statistical feature selection workflow for materials property predictions27Scope of machine learning in materials research—A review229Machine learning and atomistic origin of high dielectric permittivity in oxides18A Fast Forward Prediction Framework for Energy Materials Design Based on Machine Learning Methods25Ab initio study of stability and quadrupole coupling constants in borophosphates1Inverse design of semiconductor materials with deep generative models26Thermal Conductivity of Gallium-Indium-Tin Liquid Metal：An Experimental and Artificial Neural Network Study0Towards informatics-driven design of nuclear waste forms11Advances in materials informatics: a review42Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow33Interpretable Machine Learning To Accelerate the Analysis of Doping Effect on Li/Ni Exchange in Ni-Rich Layered Oxide Cathodes18Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks39Computing formation enthalpies through an explainable machine learning method: the case of lanthanide orthophosphates solid solutions1Predicting the properties of perovskite materials by improved compositionally restricted attention-based networks and explainable machine learning8Predicting the formation enthalpy and phase stability of (Ti,Al,TM)N (TM = III-VIB group transition metals) by high-throughput ab initio calculations and machine learning9An updated review and 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in materials science18Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow15Deep learning in-depth analysis of crystal graph convolutional neural networks: A new era in materials discovery and its applications9Investigation on the Seawater Electrolysis Catalysts of Perfect, Defective, and Mixed Cationic Spinel Znco2o4 Considering Electrode Potentials and Solvent Effect0Metallic B5c8 Monolayer as a Promising Anode Material for Na, K and Ca-Ion Batteries: A First-Principles Study0Machine learning model used to predict DFT-computed thermoelectric parameters of chalcogenide halides CuHgSCl and CuHgSI5COMPARATIVE STUDY OF IONIZATION CONSTANTS AND STRUCTURAL PROPERTIES OF OXIDATIVE AND PHENOLIC ACIDS USING AM1 AND DFT METHODS1Columnar mesomorphism in molecular alloys based on Halogen-Bridged PtII/PtIV mixed valence complexes. 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