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RESEARCH PAPER · APL Materials · 2016

Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties

Michael W. Gaultois, Anton O. Oliynyk, Arthur Mar, Taylor D. Sparks, Gregory J. Mulholland, Bryce Meredig

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Perspective: Web-based machine learning models for real-time screening of thermoelectric materials propertiesOriginalData mining our way to the next generation of thermoelectrics126New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships244Materials science with large-scale data and informatics: Unlocking new opportunities237Preface: Special Topic on Materials Genome11Gd 12 Co 5.3 Bi and Gd 12 Co 5 Bi, Crystalline Doppelgänger with Low Thermal Conductivities22High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds387Understanding and designing magnetoelectric heterostructures guided by computation: progresses, remaining questions, and perspectives148Human versus Robots in the Discovery and Crystallization of Gigantic Polyoxometalates124Human versus Robots in the Discovery and Crystallization of Gigantic Polyoxometalates39Computationally guided discovery of thermoelectric materials277Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach106Predicting displacements of octahedral cations in ferroelectric perovskites using machine learning32Materials Data Infrastructure and Materials Informatics29Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches145Big Semantic Data Processing in the Materials Design Domain5Recent advances in inorganic material thermoelectrics93Assembly of Amphiphilic Block Copolymers and Nanoparticles in Solution: Coarse-Grained Molecular Simulation Study31Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials97Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior36Machine Learning Prediction of Heat Capacity for Solid Inorganics105Multifunctional structural design of graphene thermoelectrics by Bayesian optimization136Thermoelectric Properties of CoAsSb: An Experimental and Theoretical Study9System Design, Fabrication, and Characterization of Thermoelectric and Thermal Interface Materials for Thermoelectric Devices0Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf−In System8Applying machine learning to accelerate new materials development22Machine Learning Directed Search for Ultraincompressible, Superhard Materials317How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics316The 2019 materials by design roadmap322Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering346Harnessing the Materials Project for machine-learning and accelerated discovery30NOMAD: The FAIR concept for big data-driven materials science453Not 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 Conductivity19Rapid Phase Screening via Hydride Route: A Discovery of K8–xZn18+3xSb1626MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction42Big Semantic Data Processing in the Materials Design Domain1Ternary and Quaternary Rare-Earth Transition-Metal Germanides0Towards ultimate impedance of phonon transport by nanostructure interface36Data-Driven Studies of Li-Ion-Battery Materials57Ultranarrow-Band Wavelength-Selective Thermal Emission with Aperiodic Multilayered Metamaterials Designed by Bayesian Optimization169From DFT to machine learning: recent approaches to materials science–a review849Fuzzy Logic and Fuzzy Expert System-Based Material Synthesis Methods5MatCALO: Knowledge-enabled machine learning in materials science35New frontiers for the materials genome initiative562Data analysis of multi-dimensional thermophysical properties of liquid substances based on clustering approach of machine learning13New horizons in thermoelectric materials: Correlated electrons, organic transport, machine learning, and more71Materials Discovery and Properties Prediction in Thermal Transport via Materials Informatics: A Mini Review134Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis333Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials110Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures15Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion76Recent advances and applications of machine learning in solid-state materials science2607Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design384Predicting the Structure Stability of Layered Heteroanionic Materials Exhibiting Anion Order16Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts18A high throughput molecular screening for organic electronics via machine learning: present status and perspective72Viewpoint: Atomic-Scale Design Protocols toward Energy, Electronic, Catalysis, and Sensing Applications39Uranium-based materials for thermoelectric applications4Machine Learning Approaches for Thermoelectric Materials Research231Thermoelectric Oxide Materials for Energy Conversion2Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells61Can machine learning find extraordinary materials?119Big-data driven approaches in materials science: A survey20Cost-effective materials discovery: Bayesian optimization across multiple information sources38Machine Learning Prediction for Bandgaps of Inorganic Materials30Nanostructured Polymers for Thermoelectric Conversion0A Critical Review of Machine Learning of Energy Materials606Physical and chemical descriptors for predicting interfacial thermal resistance24Compositionally-Restricted Attention-Based Network for Materials Property Prediction6Machine Learning for Structural Materials61Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509Material Descriptors for the Discovery of Efficient Thermoelectrics58Machine-Learning Assisted Screening of Energetic Materials71Data-driven discovery of 3D and 2D thermoelectric materials70A Statistical Approach for the Rapid Prediction of Electron Relaxation Time Using Elemental Representatives39A New Era of Inorganic Materials Discovery Powered by Data Science5AI Applications through the Whole Life Cycle of Material Discovery192Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials32Is Domain Knowledge Necessary for Machine Learning Materials Properties?106Machine learning in materials design: Algorithm and application*62Invited review: Machine learning for materials developments in metals additive manufacturing231Emerging materials intelligence ecosystems propelled by machine learning441The Role of Machine Learning in the Understanding and Design of Materials433Artificial neural network potential for pure zinc33A new material discovery platform of stable layered oxide cathodes for K-ion batteries78Accelerated Discovery of Thermoelectric Materials Using Machine Learning4Atomic descriptors generated from coordination polyhedra in crystal structures6Effect of the polar distortion on the thermoelectric properties of GeTe6Electronic transport descriptors for the rapid screening of thermoelectric materials21Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review44Nanostructured Polymers for Thermoelectric Conversion1Review: Simulation Models for Materials and Biomolecules5Structural diversity of the Zintl pnictides with rare-earth metals14Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis12Experiment‐Oriented Machine Learning of Polymer:Non‐Fullerene Organic Solar Cells94Machine learning for material characterization with an application for predicting mechanical properties139Thermal Transport in Polymers: A Review120Machine Learning in Magnetic Materials48A machine-learning approach to predict creep properties of Cr–Mo steel with time-temperature parameters91Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning29Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning2Challenges and opportunities for artificial intelligence and high-fidelity simulations in turbomachinery applications: A perspective12Compositionally restricted attention-based network for materials property predictions260Structure and Dynamics of Energy Materials from Machine Learning Simulations: A Topical Review†12First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning34Database Creation, Visualization, and Statistical Learning for Polymer Li+-Electrolyte Design32Catalyze Materials Science with Machine Learning67Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth16Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials46Sequential Machine Learning Applications of Particle Packing with Large Size Variations6DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions0Predicting the thermodynamic stability of perovskite oxides using multiple machine learning techniques31DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions4Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis36DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions3Data-driven thermoelectric modeling: Current challenges and prospects25A machine learning approach to predict thermal expansion of complex oxides17Applications of machine learning in computational nanotechnology13DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions28FAIR Big Data in the Materials Design Domain6Membrane-type acoustic metamaterial using cork sheets and attached masses based on reused materials79Key properties of inorganic thermoelectric materials—tables (version 1)127Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings91Predicting Material Properties Using Machine Learning for Accelerated Materials Discovery0A service recommendation system based on rough multidimensional matrix in cloud-based environment6Deep dive into machine learning density functional theory for materials science and chemistry111MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art70Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems *28Machine Learning Approaches for Accelerating the Discovery of Thermoelectric Materials15Molecular design for perovskite solar cells5Thorough Descriptor Search to Machine Learn the Lattice Thermal Conductivity of Half-Heusler Compounds9Data-Driven Enhancement of ZT in SnSe-Based Thermoelectric Systems51Theoretical Study of Ba2X6 (X = S, Se, Te) for Thermoelectric Applications Based on First-Principles Calculations and Machine Learning4Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases22Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules0Artificial intelligence assisted thermoelectric materials design and discovery4Android and Web-Based Learning Research During the Last 10 Years: How Does It Impact Physics Learning?9Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization86Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules0In silico modeling of inorganic thermoelectric materials2Electronic Configurations of 3d Transition-Metal Compounds Using Local Structure and Neural Networks9Sustainable Thermoelectric Materials Predicted by Machine Learning27Keynote Review of Latest Advances in Thermoelectric Generation Materials, Devices, and Technologies 2022169Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules18Artificial intelligence for materials research at extremes20Data-driven materials discovery and synthesis using machine learning methods14Exploiting the fraternal twin nature of thermoelectrics and topological insulators in Zintl phases as a tool for engineering new efficient thermoelectric generators26Exploring low-cost high energy NASICON cathodes for sodium-ion batteries via a combined machine-learning, ab initio , and experimental approach33Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics137Analysis and evaluation of machine learning applications in materials design and discovery33A Critical Review of Machine Learning Techniques on Thermoelectric Materials66

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