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RESEARCH PAPER · npj Computational Materials · 2022

Scale-invariant machine-learning model accelerates the discovery of quaternary chalcogenides with ultralow lattice thermal conductivity

Koushik Pal, Cheol Woo Park, Yi Xia, Jiahong Shen, Chris M. Wolverton

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Scale-invariant machine-learning model accelerates the discovery of quaternary chalcogenides with ultralow lattice thermal conductivityOriginalToward machine learning for microscopic mechanisms: A formula search for crystal structure stability based on atomic properties7Reflections on one million compounds in the open quantum materials database (OQMD)49Ultralow Lattice Thermal Conductivity in Metastable Ag2GeS3 Revealed by a Combined Experimental and Theoretical Study4Identification of high-reliability regions of machine learning predictions in materials science using perovskite oxides as an example1A Challenge toward Novel Quaternary Sulfides SrLnCuS3 (Ln = La, Nd, Tm): Unraveling Synthetic Pathways, Structures and Properties19Charge-density based convolutional neural networks for stacking fault energy prediction in concentrated alloys9Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks27High-throughput computational discovery of 3218 ultralow thermal conductivity and dynamically stable materials by dual machine learning models19High-throughput study and machine learning on MAX and MAB phases: new materials and fingerprints of superior lattice thermal conductivities42Closed‐Loop Error‐Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials14MatGPT: A Vane of Materials Informatics from Past, Present, to Future74A multiclass classification model for predicting the thermal conductivity of uranium compounds3Accelerating the prediction of stable materials with machine learning104Identification of High-Reliability Regions of Machine Learning Predictions Based on Materials Chemistry9Oxygen Vacancy Formation Energy in Metal Oxides: High-Throughput Computational Studies and Machine-Learning Predictions60End-to-end material thermal conductivity prediction through machine learning21The Substitution of Rare-Earth Gd in BaScCuTe3 Realizing the Band Degeneracy and the Point-Defect Scattering toward Enhanced Thermoelectric Performance7Electronic Properties of CrB/Co2CO2 Superlattices by Multiple Descriptor‐Based Machine Learning Combined with First‐Principles6First-principles calculations to investigate optical, phonon and electronic properties of quaternary sulfides SrRECuS3 (RE = La, Nd, Tm)10Design Principles Guided by DFT Calculations and High-Throughput Frameworks for the Discovery of New Diamond-like Chalcogenide Thermoelectric Materials14A review on the applications of graph neural networks in materials science at the atomic scale61Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review38Landscape of Thermodynamic Stabilities of A2BB′O6 Compounds15Antibonding valence states induce low lattice thermal conductivity in metal halide semiconductors27Machine learning-assisted screening of intrinsic rattling compounds with large atomic displacement0Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials188Insights into Thermal Conductivity of Pnictogen Chalcogenides: Machine Learning Stereochemically Active Lone Pairs and Hybridization11Charting the chemical space of Zintl phases with graph neural networks and bonding insights2Wide-ranging predictions of new stable compounds powered by recommendation engines12Structural, optical and thermal properties of Na2MgSnS40Advances in theory and computational methods for next-generation thermoelectric materials9Tellurides as Zintl phases?4Strong acoustic phonon suppression leads to ultralow thermal conductivity and enhanced thermoelectric performance in Ba Cu Gd Te 32Accelerated Discovery of Energy Materials via Graph Neural Network16Data-Driven Design Rules for Three-Dimensional Photonic Crystals1AI-Driven prediction system for Thermoelectric Properties: Unveiling Novel Materials for Sustainable Energy Solution in Nigeria0Thermo-mechanical stability and strong optical absorption in quaternary chalcogenides ACdCu3Se4 (A = Sc, Y): a first-principles study0Neural network framework for predicting electron transport properties of edge-defective graphene nanoribbons0Artificial Intelligence-Enabled Cross-Scale Thermal Design: A Machine-Learning-Centered Review from Atoms to Devices0

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  3. Ultralow Lattice Thermal Conductivity in Metastable Ag2GeS3 Revealed by a Combined Experimental and Theoretical Study

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