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RESEARCH PAPER · Physical review. B./Physical review. B · 2017

Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

Logan T. Ward, Ruoqian Liu, Amar Krishna, Vinay I. Hegde, Ankit Agrawal, Alok Choudhary, Chris M. Wolverton

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Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellationsOriginalAnalysis of precision and accuracy in a simple model of machine learning8Quantum Machine Learning im chemischen Raum3Quantum Machine Learning in Chemical Compound Space246Insightful classification of crystal structures using deep learning159Alchemical and structural distribution based representation for universal quantum machine learning334An online tool for predicting fatigue strength of steel alloys based on ensemble data mining101Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets364Matminer: An open source toolkit for materials data mining1068AFLOW-ML: A RESTful API for machine-learning predictions of materials properties112Applying machine learning techniques to predict the properties of energetic materials263Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials61Machine learning for molecular and materials science4884Applying machine learning to accelerate new materials development22Insightful classification of crystal structures using deep learning347Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape177Learning from Correlations Based on Local Structure: Rare-Earth Nickelates Revisited21Data-centric science for materials innovation42Strategies for accelerating the adoption of materials informatics39Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution902Deep neural networks for accurate predictions of crystal stability263Machine-learning the configurational energy of multicomponent crystalline solids75Automatic estimation of mechanical properties from fractographs using optimal anisotropic diffusion and Voronoi tessellation7Hierarchical visualization of materials space with graph convolutional neural networks69ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition509Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds117Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization40Atomic-position independent descriptor for machine learning of material properties57Recent Advances in Metallic Glass Nanostructures: Synthesis Strategies and Electrocatalytic Applications84Implementation of Isotension Ensemble in Deep Learning1Learning-in-Templates Enables Accelerated Discovery and Synthesis of New Stable Double Perovskites37Feature Engineering for Materials Chemistry—Does Size Matter?10From DFT to machine learning: recent approaches to materials science–a review848Candidate Inorganic Photovoltaic Materials from Electronic Structure-Based Optical Absorption and Charge Transport Proxies77An Atomistic Machine Learning Package for Surface Science and Catalysis25Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries199Machine Learning for Computational Heterogeneous Catalysis356Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data129General Atomic Neighborhood Fingerprint for Machine Learning-Based Methods50Deep materials informatics: Applications of deep learning in materials science294Simulation and design of energy materials accelerated by machine learning74Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential50Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics185IRNet47Energy–Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach4Recent advances and applications of machine learning in solid-state materials science2604Data-Driven Discovery of Photoactive Quaternary Oxides Using First-Principles Machine Learning49Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation337Data‐Driven Materials Science: Status, Challenges, and Perspectives700Machine learning in materials science1057DScribe: Library of descriptors for machine learning in materials science845Materials property prediction using symmetry-labeled graphs as atomic position independent descriptors17Martensite Start Temperature Predictor for Steels Using Ensemble Data Mining9Predicting the Curie temperature of ferromagnets using machine learning100A new kind of atlas of zeolite building blocks40Exploring Two-Dimensional Materials Thermodynamic Stability via Machine Learning141Unsupervised discovery of solid-state lithium ion conductors312Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning339Universality of continuous phase transitions on random Voronoi graphs3Accelerated discovery of potential ferroelectric perovskiteviaactive learning35Autonomous intelligent agents for accelerated materials discovery95Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design19In silico investigation of Cu(In,Ga)Se 2 -based solar cells3Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity123Machine learning-driven new material discovery296Using Collective Knowledge to Assign Oxidation States0Molecule Property Prediction and Classification with Graph Hypernetworks8Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions70Machine learning: Accelerating materials development for energy storage and conversion370MaterialNet: A web-based graph explorer for materials science data10Machine-Learning-Accelerated Perovskite Crystallization144Materials Image Informatics Using Deep Learning11Topology-Based Machine Learning Strategy for Cluster Structure Prediction46Machine learning in materials genome initiative: A review231Crystal Site Feature Embedding Enables Exploration of Large Chemical Spaces44Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches161Learning from the Machine: Uncovering Sustainable Nanoparticle Design Rules17Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery281Machine Learning Assisted Discovery of New Metal-free Perovskites for Blue Light Emission0Detection of topological materials with machine learning59Machine-Learning Assisted Screening of Energetic Materials71Big-Data Science in Porous Materials: Materials Genomics and Machine Learning597Exploring chemical compound space with quantum-based machine learning367Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks37Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction144Accelerated Discovery of Novel Inorganic Materials with Desired Properties Using Active Learning20Accelerated prediction of Vickers hardness of Co- and Ni-based superalloys from microstructure and composition using advanced image processing techniques and machine learning78Machine learning assisted materials design and discovery for rechargeable batteries466A critical examination of compound stability predictions from machine-learned formation energies268A New Era of Inorganic Materials Discovery Powered by Data Science5Machine learning approaches for the prediction of materials properties255Applications of machine learning in metal-organic frameworks225Computational thermodynamics and its applications172Predicting Inorganic Photovoltaic Materials with Efficiencies >26% via Structure-Relevant Machine Learning and Density Functional Calculations28Carbon materials for high-performance potassium-ion energy-storage devices45Knowledge-Based Descriptor for the Compositional Dependence of the Phase Transition in BaTiO3-Based Ferroelectrics22Machine learning for metallurgy I. 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stabilities: from sub-nanometer clusters to extended surfaces16DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers13Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning8Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures96An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature49Inverse design of nanoporous crystalline reticular materials with deep generative models399Topological representations of crystalline compounds for the machine-learning prediction of materials properties112Materials discovery through machine learning formation energy77Recent Advances in Screening Lithium Solid-State Electrolytes Through Machine Learning25Enabling deeper learning on big data for materials informatics applications67Atom classification with Machine Learning and correlations among physical properties of ZnO nanoparticle15Rare-class learning over Mg-doped ZnO nanoparticles32Modelling and optimization of microhardness of electroless Ni–P–TiO2 composite coating based on machine learning approaches and RSM82Building Machine Learning systems for multi-atoms structures: CH3NH3PbI3 perovskite nanoparticles36Global Property Prediction: A Benchmark Study on Open-Source, Perovskite-like Datasets14Machine Learning in Magnetic Materials48Machine learning modeling of materials with a group-subgroup structure2Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys33Materials Representation and Transfer Learning for Multi-Property Prediction4Materials Representation and Transfer Learning for Multi-Property Prediction1Electronic-structure methods for materials design253Materials representation and transfer learning for multi-property prediction71Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds17Proxima9Automatic Migration Path Exploration for Multivalent Battery Cathodes using Geometrical Descriptors21Emerging artificial intelligence in piezoelectric and triboelectric nanogenerators154Rational Solid-State Synthesis Routes for Inorganic Materials115First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning34Catalyze Materials Science with Machine Learning66Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks92A universal model for the formation energy prediction of inorganic compounds1Machine learning for materials discovery: Two-dimensional topological insulators71Machine Learning-Aided Materials Design Platform for Predicting the Mechanical Properties of Na-Ion Solid-State Electrolytes36Ab Initio Machine Learning in Chemical Compound Space195Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening52Searching for Mechanically Superior Solid-State Electrolytes in Li-Ion BatteriesviaData-Driven Approaches60Synthesizable Double Perovskite Oxide Search via Machine Learning and High‐Throughput Computational Screening18A geometric-information-enhanced crystal graph network for predicting properties of materials115Machine learning guided discovery of super-hard high entropy ceramics39Machine learning for the design and discovery of zeolites and porous crystalline materials41Predicting the thermodynamic stability of perovskite oxides using multiple machine learning techniques31Accurate bandgap predictions of solids assisted by machine learning44Machine Learning with Multilevel Descriptors for Screening of Inorganic Nonlinear Optical Crystals35Adaptive Learning Framework in Prediction and Validation of Gibbs Free Energy for Inorganic Crystalline Solids12A data-driven approach to approximate the correlation functions in cluster variation method5

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