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RESEARCH PAPER · Chemistry of Materials · 2016

Classifying Crystal Structures of Binary Compounds AB through Cluster Resolution Feature Selection and Support Vector Machine Analysis

Anton O. Oliynyk, Lawrence Asamoah Adutwum, James J. Harynuk, Arthur Mar

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Classifying Crystal Structures of Binary Compounds AB through Cluster Resolution Feature Selection and Support Vector Machine AnalysisOriginalEstimation of start and stop numbers for cluster resolution feature selection algorithm: an empirical approach using null distribution analysis of Fisher ratios15Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC95Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches145Optimization-based design of crude oil distillation units using surrogate column models and a support vector machine76Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons201Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf−In System8Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning387Machine Learning Directed Search for Ultraincompressible, Superhard Materials317How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics316Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering346Discovery of Descriptors for Stable Monolayer Oxide Coatings through Machine Learning17Hard and superhard materials: A computational perspective57A Study of the Utility of a Machine-Learning Approach Applied to the Prediction of Site Occupancy and New Members of the Half-Heusler Family0Machine learning for renewable energy materials312Ternary and Quaternary Rare-Earth Transition-Metal Germanides0Data-Driven Studies of Li-Ion-Battery Materials57Machine Learning-Directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-Confined Regime63Solving the Coloring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation29Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures15Recent advances and applications of machine learning in solid-state materials science2604Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction55Virtual Issue on Machine-Learning Discoveries in Materials Science29Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells61Machine Learning for Catalysis Informatics: Recent Applications and Prospects611Navigating the design space of inorganic materials synthesis using statistical methods and machine learning43The CSD and knowledge databases: from answers to questions22Discovering Intermetallics Through Synthesis, Computation, and Data‐Driven Analysis29Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches161Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices508A QMC-Deep Learning Method for Diffusivity Estimation in Random Domains2Machine-Learning Assisted Screening of Energetic Materials71A New Era of Inorganic Materials Discovery Powered by Data Science5Accelerating inverse crystal structure prediction by machine learning: A case study of carbon allotropes26Structure prediction of two-dimensional materials based on neural network-driven evolutionary technique4Machine learning in materials design: Algorithm and application*62A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds58Determining usefulness of machine learning in materials discovery using simulated research landscapes15Accelerated crystal structure prediction of multi-elements random alloy using expandable features21A Vacancy-Driven Intermetallic Phase: Rh3Cd5−δ (δ ∼ 0.56)9Halide Replacement with Complete Preservation of Crystal Lattice in Mixed‐Anion Lanthanide Oxyhalides26Halide Replacement with Complete Preservation of Crystal Lattice in Mixed‐Anion Lanthanide Oxyhalides1Extending Shannon's ionic radii database using machine learning81An efficient and accurate numerical determination of the cluster resolution metric in two dimensions12AB2X6 Compounds and the Stabilization of Trirutile Oxides7Progress in Research on Artificial Intelligence Applied to Polymorphism and Cocrystal Prediction55Machine Learning in Solid‐State Chemistry: Heusler Compounds0Prediction of the Curie temperatures of ferroelectric solid solutions using machine learning methods19Structural and thermal stability of B20-type high-pressure phases FeGe and MnGe6Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth15Development of artificial intelligence based model for the prediction of Young’s modulus of polymer/carbon-nanotubes composites47Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry80Innovative Materials Science via Machine Learning194Search methods for inorganic materials crystal structure prediction27Predicting the thermodynamic stability of perovskite oxides using multiple machine learning techniques31Deformation potential extraction and computationally efficient mobility calculations in silicon from first principles67Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors38Prediction Model for Tensile Modulus of Carbon Nanotube–Polymer Composites4Hardness prediction of high entropy alloys with machine learning and material descriptors selection by improved genetic algorithm97Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning33Crystal structure prediction by combining graph network and optimization algorithm114Formation and stability of Rh2Cd5 and its strucural correlation with RhCd and Rh3Cd5−δ (δ ∼ 0.56)2Green Chemistry Applied to Transition Metal Chalcogenides through Synthesis, Design of Experiments, Life Cycle Assessment, and Machine Learning5Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods128Discovering constitutive equations of crystal structures by sparse identification3Data-driven materials discovery and synthesis using machine learning methods14Machine learning descriptors in materials chemistry: prediction and experimental validation synthesis of novel intermetallic UCd36Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques2Revealing Hidden Patterns through Chemical Intuition and Interpretable Machine Learning: A Case Study of Binary Rare-Earth Intermetallics RX16Prediction of sintered density of binary W(Mo) alloys using machine learning27Data-driven forecasting of nonequilibrium solid-state dynamics3Geometries and electronic structures of Zr n Cu (n = 2–12) clusters: A joint machine-learning potential density functional theory investigation2Outliers in Shannon’s effective ionic radii table and the table extension by machine learning34Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques21Machine-learning surrogate model for accelerating the search of stable ternary alloys5A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films19Machine Learning Assisted Density Prediction of Cathodes Alloy Matrix0Advances of machine learning in materials science: Ideas and techniques105Influence of the Homoatomic and Multicenter Bonding on the Formation of CsCl‐type RhCd1Machine learning descriptors in materials chemistry used in multiple experimentally validated studies: Oliynyk elemental property dataset31Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data6Multi-scale computational study of high-temperature corrosion and the design of corrosion-resistant alloys33Advances in machine learning methods in copper alloys: a review9Machine learning prediction of materials properties from chemical composition: Status and prospects29The impact of cluster resolution feature selection on pattern recognition and classification for detecting Sudan dye adulteration in palm oil3Composition and structure analyzer/featurizer for explainable machine-learning models to predict solid state structures13Recent strides in artificial intelligence for predicting thermoelectric properties and materials discovery9GCPNet: An interpretable Generic Crystal Pattern graph neural Network for predicting material properties13Unsupervised Machine Learning Prediction of a Novel 1:3 Intermetallic Phase with the Synthesis of TbIr 3 (PuNi 3 -type) as Experimental Validation7Hierarchy-boosted funnel learning for identifying semiconductors with ultralow lattice thermal conductivity8Exploring Feature Engineering for Crystal Structure Classification: Interactive Applications of PCA and PLS-DA Clustering2Disentangling Structural Patterns in Quaternary Germanides with Materials Informatics and Exploratory Synthesis: RE 4 IrInGe 4 ( RE = Y, Ce–Nd, Sm, Gd, Ho–Er) Series1CA-SMART: An active learning framework for accelerating materials discovery under resource constraints1D 3 REAM: a framework for full-space inverse materials design with target properties via machine learning models and global optimization algorithms0Evaluation of machine learning models in the search of interpretable structure maps of perovskites and spinels0

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  2. Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC

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  3. Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches

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  4. Optimization-based design of crude oil distillation units using surrogate column models and a support vector machine

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  5. Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons

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  6. Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf−In System

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  7. Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning

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  8. Machine Learning Directed Search for Ultraincompressible, Superhard Materials

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  9. How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics

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  18. Solving the Coloring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation

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  19. Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures

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  21. Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction

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