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RESEARCH PAPER · Chemistry of Materials · 2018

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

Jake Graser, Steven K. Kauwe, Taylor D. Sparks

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Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New HorizonsOriginalMachine Learning Prediction of Heat Capacity for Solid Inorganics105Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering346Data mining in photocatalytic water splitting over perovskites literature for higher hydrogen production76Extracting 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 Conductivity19A machine learning approach for predicting formation enthalpy: A case study of Mackay-type approximants of icosahedral quasicrystals2Ternary and Quaternary Rare-Earth Transition-Metal Germanides0Creating Machine Learning-Driven Material Recipes Based on Crystal Structure46Data-Driven Studies of Li-Ion-Battery Materials57Data-Mining for Processes in Chemistry, Materials, and Engineering43“Teamwork Makes the Dream Work”: Tribal Competition Evolutionary Search as a Surrogate for Free-Energy-Based Structural Predictions3Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments39Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures15Energy–Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach4Recent advances and applications of machine learning in solid-state materials science2607Recent progress on the prediction of two-dimensional materials using CALYPSO32Virtual Issue on Machine-Learning Discoveries in Materials Science29Viewpoint: Atomic-Scale Design Protocols toward Energy, Electronic, Catalysis, and Sensing Applications39Uranium-based materials for thermoelectric applications4Machine Learning Approaches for Thermoelectric Materials Research231Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Can machine learning find extraordinary materials?119In silico investigation of Cu(In,Ga)Se 2 -based solar cells3Machine-learning-assisted search for functional materials over extended chemical space25Non-precious-metal catalysts for alkaline water electrolysis: operando characterizations, theoretical calculations, and recent advances884The CSD and knowledge databases: from answers to questions22New Quasicrystal Approximant in the Sc–Pd System: From Topological Data Mining to the Bench15Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods52Compositionally-Restricted Attention-Based Network for Materials Property Prediction6Machine learning: Accelerating materials development for energy storage and conversion370Phase diagrams classification based on machine learning and phenomenological investigation of physical properties in K1 − xNaxNbO3 thin films13Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry62Machine-Learning Assisted Screening of Energetic Materials71Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction144Importance of structural deformation features in the prediction of hybrid perovskite bandgaps38Machine learning study of magnetism in uranium-based compounds23An adaptive design approach for defects distribution modeling in materials from first-principle calculations20A New Era of Inorganic Materials Discovery Powered by Data Science5Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning40AI Applications through the Whole Life Cycle of Material Discovery192A Genetic Algorithmic Approach to Determine the Structure of Li–Al Layered Double Hydroxides8Machine learning in materials design: Algorithm and application*62The XtalOpt Evolutionary Algorithm for Crystal Structure Prediction89Artificial neural network potential for pure zinc33CRYSPNet: Crystal structure predictions via neural networks42Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models120Machine learning phases and criticalities without using real data for training12Machine learning for molecular thermodynamics65Data-driven enhancement of cubic phase stability in mixed-cation perovskites30Predicting stable crystalline compounds using chemical similarity113Machine learning for perovskite materials design and discovery536Machine learning approach for the prediction and optimization of thermal transport properties71Machine learning for material characterization with an application for 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