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RESEARCH PAPER · Accounts of Chemical Research · 2017

Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches

Anton O. Oliynyk, Arthur Mar

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Discovery of Intermetallic Compounds from Traditional to Machine-Learning ApproachesOriginalCan machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery264Machine Learning and High-Throughput Approaches to Magnetism6Machine learning for predicting product distributions in catalytic regioselective reactions41Revealing Tendencies in the Electronic Structures of Polar Intermetallic Compounds38Predicting the stability of ternary intermetallics with density functional theory and machine learning40Machine Learning Prediction of Heat Capacity for Solid Inorganics105Quaternary rare-earth selenides Ba2REGaSe5 and Ba2REInSe56Crystal Structure Prediction via Deep Learning355Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf−In System8Perspective: Superhard metal borides: A look forward114How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics316Looking for new thermoelectric materials among the TMX intermetallics using high-throughput calculations25Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations123Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering346Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning4Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity19Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities423Machine-Learning-Based Cyclic Voltammetry Behavior Model for Supercapacitance of Co-Doped Ceria/rGO Nanocomposite42Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds116A Study of the Utility of a Machine-Learning Approach Applied to the Prediction of Site Occupancy and New Members of the Half-Heusler Family0Ternary and Quaternary Rare-Earth Transition-Metal Germanides0Crystal Growth of Intermetallics from the Joint Flux: Exploratory Synthesis through the Control of Valence Electron Count14Data-Driven Studies of Li-Ion-Battery Materials57ReGa0.4Ge0.6: Intermetallic Compound with Pronounced Covalency in the Bonding Pattern4Templating Structural Progessions in Intermetallics: How Chemical Pressure Directs Helix Formation in the Nowotny Chimney Ladders11Atomic Arrangement Engineering of Metallic Nanocrystals for Energy-Conversion Electrocatalysis305Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals1525Machine 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 Structures15Emerging role of machine learning in light-matter interaction83A high throughput molecular screening for organic electronics via machine learning: present status and perspective72Virtual Issue on Machine-Learning Discoveries in Materials Science29Machine Learning Approaches for Thermoelectric Materials Research231Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning97Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Rapid Identification of X-ray Diffraction Spectra Based on Very Limited Data by Interpretable Convolutional Neural Networks1Machine Learning and High-Throughput Approaches to Magnetism5Navigating the design space of inorganic materials synthesis using statistical methods and machine learning43Unraveling the role of bonding chemistry in connecting electronic and thermal transport by machine learning33Growing field of materials informatics: databases and artificial intelligence22A Critical Review of Machine Learning of Energy Materials606Machine learning: Accelerating materials development for energy storage and conversion370Discovering Intermetallics Through Synthesis, Computation, and Data‐Driven Analysis29Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks112Machine learning in materials genome initiative: A review231Machine-Learning-Guided Morphology Engineering of Nanoscale Metal-Organic Frameworks83Nanostructural metallic materials: Structures and mechanical properties326Efficient Syntheses of 2D Materials from Soft Layered Composites Guided by Yield Prediction Model: Potential of Experiment‐Oriented Materials Informatics22Machine learning study of magnetism in uranium-based compounds23Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning40Nowotny Chimney Ladder Phases with Group 5 Metals: Crystal and Electronic Structure and Relations to the CrSi2 Structure Type5Machine learning in experimental materials chemistry66Electron-Precise Semiconducting ReGa2Ge: Extending the IrIn3 Structure Type to Group 7 of the Periodic Table16Evaluation-oriented exploration of photo energy conversion systems: from fundamental optoelectronics and material screening to the combination with data science84Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation44Machine learning formation enthalpies of intermetallics22Machine learning for halide perovskite materials130Chalcophosphates: A Treasure House of Infrared Nonlinear Optical Materials75Exfoliation Chemistry of Soft Layered Materials toward Tailored 2D Materials31Intermetallic compounds with non-metallic properties14Structural diversity of the Zintl pnictides with rare-earth metals14Materials discovery through machine learning formation energy78Experiment‐Oriented Machine Learning of Polymer:Non‐Fullerene Organic Solar Cells94Machine Learning‐Aided Crystal Facet Rational Design with Ionic Liquid Controllable Synthesis34Research on Application of Machine Learning Technology in New Material System0Visual Analysis on Machine Learning Assisted Prediction of Ionic Conductivity for Solid-State Electrolytes3Predicting inorganic dimensionality in templated metal oxides8Challenges and opportunities for artificial intelligence and high-fidelity simulations in turbomachinery applications: A perspective12Predictive Synthesis54Structural studies of ligand stabilized Ni/Ga clusters by means of vibrational spectroscopy and theoretical calculations4Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment22Intermetallic Compounds: Liquid‐Phase Synthesis and Electrocatalytic Applications24Materials Informatics for 2D Materials Combined with Sparse Modeling and Chemical Perspective: Toward Small-Data-Driven Chemistry and Materials Science52How to Look for Compounds: Predictive Screening and in situ Studies in Na−Zn−Bi System8Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling18matExplorer: Visual Exploration on Predicting Ionic Conductivity for Solid-state Electrolytes8Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells90Introductory chapter1Sparse modeling for small data: case studies in controlled synthesis of 2D materials26Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks98Material Design Towards Theory-Experiment Congruence: A Thermodynamically Stable Co-Cr-Fe-Al Heusler Phase0Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46Diversity in Crystal Structure and Physical Properties of RETX 3 ( RE – Rare Earth, T – Transition Metal, X – Main Group Element) Intermetallics14High‐entropy intermetallics: from alloy design to structural and functional properties70Generation of functional oligopeptides that promote osteogenesis based on unsupervised deep learning of protein IDRs22Green Chemistry Applied to Transition Metal Chalcogenides through Synthesis, Design of Experiments, Life Cycle Assessment, and Machine Learning5Predicting Material Properties by Deep Graph Networks7Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Effects of data bias on machine-learning–based material discovery using experimental property data24Research Network Analysis and Machine Learning on Heusler Alloys14The Crystal and Electronic Structure of Re23co7in20 (Re = Gd–Tm, Lu): A New Structure Type Based on Intergrowth of Alb2- and Cscl-Type Related Slabs0Machine learning descriptors in materials chemistry: prediction and experimental validation synthesis of novel intermetallic UCd36Revealing Hidden Patterns through Chemical Intuition and Interpretable Machine Learning: A Case Study of Binary Rare-Earth Intermetallics RX16Application of machine learning to reveal relationship between processing-structure-property for polypropylene injection molding38Graph neural network with self-attention for material discovery2Machine learning for prediction of atom-projected properties4Fundamental design strategies for advancing the development of high entropy alloys for thermo-mechanical application: A critical review110The crystal and electronic structure of RE23Co6.7In20.3 (RE = Gd–Tm, Lu): A new structure type based on intergrowth of AlB2- and CsCl-type related slabs10Machine-learning guided prediction of thermoelectric properties of topological insulator Bi 2 Te 3− x Se x26Scaling Discovery Infrastructures: Computational Resource Allocation in Materials Engineering and Computational Data0Surface energy prediction and Winterbottom morphology evolution analysis in Winterbottom construction on various crystal orientations using machine learning4Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning6Deciphering the Atomic Size Factor in the TiNiSi type Zintl phase CaAg0.5Al0.5Ge0Deciphering the Atomic Size Factor in the TiNiSi type Zintl phase CaAg0.5Al0.5Ge1Europium-rich ternary phases with noble metals Eu2M2X (M = Pd, Pt; X = Al, Ga, Ge, Cd), and their structural relationship to the Laves phase Ca2Pt3Al1–xAgx1Europium-rich ternary phases with noble metals Eu2M2X (M = Pd, Pt; X = Al, Ga, Ge, Cd), and their structural relationship to the Laves phase Ca2Pt3Al1–xAgx1Deciphering the Atomic Size Factor in Quasi-ternary Zintl phases RAg0.5Al0.5Ge (R = Ca, Eu) with the TiNiSi type Structure0Stabilization of the elusive or non-existent “YbMnPt” phase by chemical heterogeneity: The pseudo-ternary phases Eu0.28Yb0.72MnPt and YbCd0.13Mn0.87Pt with TiNiSi type structure1Unveiling Stability Factors in Sn(II)-Containing Oxides: Discovery of a Polar Tin Titanate and Photocatalytic Activity for Overall Water Splitting7Discovery of Ternary Antimonides A–Al–Sb (A = Rb or Cs) with Desired Structural Motifs Guided by Machine Learning5Partial ordering of isovalent Cd and Mn atoms in the quater-nary phase Eu2Mn1–xCd1+xPt2 (x = 0.11) with LaNiAl type struc-ture1Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development51The rise of semi-metal electronics36Interface Nucleus Complementarity: An Iterative Process for the Discovery of Modular Intermetallics Guided by Chemical Pressure4First-Principles Study of Novel BaLnAgSe3 (Ln = Er, Gd) Active UV-reflecting Materials for Optoelectronic and Thermoelectric Applications5Machine Learning-Based Investigation of Atomic Packing Effects: Chemical Pressures at the Extremes of Intermetallic Complexity3Systematic searches for new inorganic materials assisted by materials informatics8Dielectric tensor prediction for inorganic materials using latent information from preferred potential12Advancing the next generation of high-performance metal matrix composites through metal particle reinforcement74Machine learning prediction of materials properties from chemical composition: Status and prospects29Bridging current and future innovations to unlock the potential of multifunctional materials for sustainable energy applications28Structural chemistry of intermetallic compounds for active site design in heterogeneous catalysis24Ternary materials discovery using human-in-the-loop generative machine learning3Artificial intelligence-driven phase stability evaluation and new dopants identification of hafnium oxide-based ferroelectric materials27An optimized strategy for density prediction of intermetallics across varied crystal structures via graph neural network2Interplay of Crystal Structure and Magnetic Properties of the Eu5.08-xSrxAl3Sb6 Solid Solution2Synthesis, Crystal and Electronic Structure of Ca4CdIn2Ge4: A Quaternary Variant of the Monoclinic Mg5Si6 Structure1Multi-wire additive manufacturing: A comprehensive review on materials, microstructure, methodological advances, and applications43Predictive Modeling of Half-Metallicity in Heusler Compound through Machine Learning Modeling1Recent progress in the design and application of machine learning for the hydrogen evolution reaction in electrocatalysis and photocatalysis3Material design toward a thermodynamically stable Co–Cr–Fe–Al Heusler-type phase1Comprehensive Review on the Use of Machine Learning Techniques Applied to the Ultrasound Data for the Characterisation of Porosity across Carbon Fibre Reinforced Polymer Layers7GAN-driven discovery of low-cost non-noble metallic electrocatalysts for glycerol electroreduction3ABO3 materials clustering with quantum swap-test algorithm2Explainable Recommendation Engines to Predict Complex Intermetallics: Synthesis and Characterization of Gd10RuCd3, a Neutron Absorption Material1Dataset of prototype structures adopted by intermetallic compounds with AB stacking4Encapsulation of Ba in InSb Framework Introduces Chirality in Clathrate-Like BaIn 4 Sb 42Machine learning in nanoscience and beyond: Workflows, data processing, XAI and ITAP metrics, language-based models5High-entropy alloy electrocatalysts for water splitting: A systematic review with perspectives on machine learning and future design strategies9Exploring Feature Engineering for Crystal Structure Classification: Interactive Applications of PCA and PLS-DA Clustering2One Cation Makes a Difference: Structure–Thermoelectric Interplay in Pseudo–Rock Salt Intermetallic Eu 5– x A x Al 3 Sb 6 (A = Sr and Yb)1Disentangling Structural Patterns in Quaternary Germanides with Materials Informatics and Exploratory Synthesis: RE 4 IrInGe 4 ( RE = Y, Ce–Nd, Sm, Gd, Ho–Er) Series1SemiConLens: Visual Analytics for 2D Semiconductor Discovery0

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