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RESEARCH PAPER · Journal of the American Chemical Society · 2018

Machine Learning Directed Search for Ultraincompressible, Superhard Materials

Aria Mansouri Tehrani, Anton O. Oliynyk, Marcus E. Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D. Sparks, Jakoah Brgoch

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Machine Learning Directed Search for Ultraincompressible, Superhard MaterialsOriginalBrgoch Superhard Materials Training Data0Notizen aus der Chemie0Extracting 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 Conductivity19Hard and superhard materials: A computational perspective57The vacancy ordering produces a new cubic monocarbide: ReC17Prediction of Carbon Dioxide Adsorption via Deep Learning128Prediction of Carbon Dioxide Adsorption via Deep Learning74Emerging Investigators in Solid-State Inorganic Chemistry2A Study of the Utility of a Machine-Learning Approach Applied to the Prediction of Site Occupancy and New Members of the Half-Heusler Family0Designing promising molecules for organic solar cells via machine learning assisted virtual screening115Lattice strain and texture analysis of superhard Mo 0.9 W 1.1 BC and ReWC 0.8 via diamond anvil cell deformation2Ternary and Quaternary Rare-Earth Transition-Metal Germanides0Finding the Right Bricks for Molecular Legos: A Data Mining Approach to Organic Semiconductor Design61Data-Driven Studies of Li-Ion-Battery Materials57Machine Learning-Aided Design of Materials with Target Elastic Properties36Paradigm shift in electron-based crystallography via machine learning43From DFT to machine learning: recent approaches to materials science–a review848Rocketsled: a software library for optimizing high-throughput computational searches19Data-Mining for Processes in Chemistry, Materials, and Engineering43Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals1525Synthesis and Characterization of Single-Phase Metal Dodecaboride Solid Solutions: Zr1–xYxB12 and Zr1–xUxB1228Synthesis and characterization of WB2-WB3-B4C hard composites22Machine learning for hierarchical prediction of elastic properties in Fe-Cr-Al system28Fast and Accurate Artificial Neural Network Potential Model for MAPbI3 Perovskite Materials38Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning116Artificial intelligence for materials discovery120Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures15Atom table convolutional neural networks for an accurate prediction of compounds properties98Recent advances and applications of machine learning in solid-state materials science2604Atomic Substitution to Balance Hardness, Ductility, and Sustainability in Molybdenum Tungsten Borocarbide16Mechanism for unconventional nonlinear elasticity6Machine Learning Optimization of p-Type Transparent Conducting Films42Structural, mechanical, and thermodynamic properties of R-3m ReB4 under high pressure2Predicting superhard materials via a machine learning informed evolutionary structure search121Machine Learning Interatomic Potentials as Emerging Tools for Materials Science1037Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonomous Discovery in the Chemical Sciences Part I: Progress316Virtual Issue on Machine-Learning Discoveries in Materials Science29Viewpoint: Atomic-Scale Design Protocols toward Energy, Electronic, Catalysis, and Sensing Applications39Machine learning and artificial neural network accelerated computational discoveries in materials science151Five High-Impact Research Areas in Machine Learning for Materials Science72The influence of high pressure on the structural stability, Vickers hardness and mechanical properties of Re and Ru dodecaborides11Role of W-site substitution on mechanical and electronic properties of cubic tungsten carbide5Evaluating Thermal Quenching Temperature in Eu 3+ -Substituted Oxide Phosphors via Machine Learning81Can machine learning find extraordinary materials?119Accurate predictions of aqueous solubility of drug molecules via the multilevel 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electrodes for redox flow batteries82AB2X6 Compounds and the Stabilization of Trirutile Oxides7Catalyze Materials Science with Machine Learning66Deep Extrapolation for Attribute-Enhanced Generation10Machine learning and evolutionary prediction of superhard B-C-N compounds68Machine Learning Approach to Analyze the Surface Properties of Biological Materials30Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers61Strengthening Superhard Materials by Nanostructure Engineering3Deep learning framework for material design space exploration using active transfer learning and data augmentation178Sequential Machine Learning Applications of Particle Packing with Large Size Variations6Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges66Innovative Materials Science via Machine Learning194Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control68DiSCoVeR: a Materials Discovery Screening Tool for 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  1. Brgoch Superhard Materials Training Data

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  2. Notizen aus der Chemie

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  3. Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning

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  4. Not 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 Conductivity

    Inorganic Chemistry · 2018-10-26

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  5. Hard and superhard materials: A computational perspective

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  6. The vacancy ordering produces a new cubic monocarbide: ReC

    Materials Today Physics · 2018-12-01

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  7. Prediction of Carbon Dioxide Adsorption via Deep Learning

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  12. Lattice strain and texture analysis of superhard Mo 0.9 W 1.1 BC and ReWC 0.8 via diamond anvil cell deformation

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  13. Ternary and Quaternary Rare-Earth Transition-Metal Germanides

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  14. Finding the Right Bricks for Molecular Legos: A Data Mining Approach to Organic Semiconductor Design

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  17. Paradigm shift in electron-based crystallography via machine learning

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  47. Accurate predictions of aqueous solubility of drug molecules via the multilevel graph convolutional network (MGCN) and SchNet architectures

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  49. Graph convolutional neural networks with global attention for improved materials property prediction

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  50. Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials

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