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RESEARCH PAPER · Energy & Environmental Science · 2016

Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

Austin D. Sendek, Qian Yang, Ekin D. Cubuk, Karel-Alexander N. Duerloo, Yi Ping Cui, Evan J. Reed

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Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materialsOriginalFrom molecules to dollars: integrating molecular design into thermo-economic process design using consistent thermodynamic modeling62Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment192Representations in neural network based empirical potentials49Materials discovery and design using machine learning1266Structural, Chemical, and Dynamical Frustration: Origins of Superionic Conductivity in closo -Borate Solid Electrolytes177Role of anisotropy in determining stability of electrodeposition at solid-solid interfaces24A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks73Synthesis and Structures of Novel Solid-State Electrolytes1The Materials Project: Accelerating Materials Design Through Theory-Driven Data and Tools41Prospective improvements in cost and cycle life of off-grid lithium-ion battery packs: An analysis informed by expert elicitations100An effective method to screen sodium-based layered materials for sodium ion batteries100Composition design for (PrNd–La–Ce) 2 Fe 14 B melt-spun magnets by machine learning technique5Computational Screening of 2D Materials and Rational Design of Heterojunctions for Water Splitting Photocatalysts211Design Strategies, Practical Considerations, and New Solution Processes of Sulfide Solid Electrolytes for All‐Solid‐State Batteries589A strategy to apply machine learning to small datasets in materials science776Designing Ionic Conductors: The Interplay between Structural Phenomena and Interfaces in Thiophosphate-Based Solid-State Batteries171Modeling and theoretical design of next-generation lithium metal batteries93Designing for Democratization: Introducing Novices to Artificial Intelligence Via Maker Kits4High Capacity Garnet-Based All-Solid-State Lithium Batteries: Fabrication and 3D-Microstructure Resolved Modeling127Modeling lithium-ion solid-state electrolytes with a pinball model48Data-driven material discovery for photocatalysis: a short review20High‐throughput computational screening of layered and two‐dimensional materials61Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations123Machine learning-assisted discovery of many new solid Li-ion conducting materials4Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes279Understanding migration barriers for monovalent ion insertion in transition metal oxide and phosphate based cathode materials: A DFT study90Computation-Accelerated Design of Materials and Interfaces for All-Solid-State Lithium-Ion Batteries398Opportunities for Rechargeable Solid-State Batteries Based on Li-Intercalation Cathodes194Computational analysis and identification of battery materials15CoSe2-Decorated NbSe2 Nanosheets Fabricated via Cation Exchange for Li Storage24Integrated computational materials engineering for advanced materials: A brief review182Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials328Revealing the Spectrum of Unknown Layered Materials with Superhuman Predictive Abilities27Data‐Driven Materials Exploration for Li‐Ion Conductive Ceramics by Exhaustive and Informatics‐Aided Computations52Empirical modeling of dopability in diamond-like semiconductors46Discovery and design of lithium battery materials via high-throughput modeling4Effect of Crystallinity on Li Adsorption in Polyethylene Oxide43Accelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning173Recent Advances in Metallic Glass Nanostructures: Synthesis Strategies and Electrocatalytic Applications84Correlating lattice distortions, ion migration barriers, and stability in solid electrolytes126Machine learning for renewable energy materials312Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents68Data-Driven Studies of Li-Ion-Battery Materials57Theoretical investigation of lithium ions’ nucleation performance on metal-doped Cu surfaces26Optimising lithium-ion cell design for plug-in hybrid and battery electric vehicles61First-principles prediction of high oxygen-ion conductivity in trilanthanide gallates Ln 3 GaO 611Modeling Solid State Batteries2Computational Screening of Cathode Coatings for Solid-State Batteries451Data-driven prediction of battery cycle life before capacity degradation3169Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries199Computational Screening of Layered Materials for Multivalent Ion Batteries63High-Throughput Screening of Solid-State Li-Ion Conductors Using Lattice-Dynamics Descriptors262Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data129Accelerated Durability Test for High‐Surface‐Area Oxyhydroxide Nickel Supported on Raney Nickel as Catalyst for the Alkaline Oxygen Evolution Reaction28Simulation and design of energy materials accelerated by machine learning74Alternative materials for perovskite solar cells from materials informatics29Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design384Data-Driven Systematic Search of Promising Photocatalysts for Water Splitting under Visible Light51Improved Representations of Heterogeneous Carbon Reforming Catalysis Using Machine Learning27Highly efficient evaluation of diffusion networks in Li ionic conductors using a 3D-corrugation descriptor7Machine learning-based design of porous graphene with low thermal conductivity99Computationally Guided Discovery of the Sulfide Li3AlS3 in the Li–Al–S Phase Field: Structure and Lithium Conductivity25Battery Safety: Data-Driven Prediction of Failure61Unsupervised discovery of solid-state lithium ion conductors312Simulating Diffusion Properties of Solid‐State Electrolytes via a Neural Network Potential: Performance and Training Scheme51Understanding interface stability in solid-state batteries1219Big-data driven approaches in materials science: A survey20Fast material search of lithium ion conducting oxides using a recommender system27High-throughput computational screening for solid-state Li-ion conductors141Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity123Machine learning in materials modeling—fundamentals and the opportunities in 2D materials22Machine-enabled inverse design of inorganic solid materials: promises and challenges190Progress and perspectives on halide lithium conductors for all-solid-state lithium batteries755The Materials Project: Accelerating Materials Design Through Theory-Driven Data and Tools60Comment on “Accelerated Discovery of New 8-Electron Half-Heusler Compounds as Promising Energy and Topological Quantum Materials”28Reply to “Comment on ‘Accelerated Discovery of New 8-Electron Half-Heusler Compounds as Promising Energy and Topological Quantum Materials’”7A Critical Review of Machine Learning of Energy Materials606Essential structural and experimental descriptors for bulk and grain boundary conductivities of Li solid electrolytes11Designing solid-state electrolytes for safe, energy-dense batteries2372Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions70Artificial intelligence and machine learning for targeted energy storage solutions64Challenges in Lithium Metal Anodes for Solid-State Batteries523Machine learning: Accelerating materials development for energy storage and conversion3701．固体電池開発に向けたリチウム導電体探索—古典的手法から機械学習の活用まで—0The solid-state Li-ion conductor Li7TaO6: A combined computational and experimental study8Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals49Trends in 3D Printing Processes for Biomedical Field: Opportunities and Challenges210Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine Learning51Quantifying the Search for Solid Li-Ion Electrolyte Materials by Anion: A Data-Driven Perspective2Knot Morphing Algorithm for Quantum `Fragile Topology'0Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique89Machine learning-assisted cross-domain prediction of ionic conductivity in sodium and lithium-based superionic conductors using facile descriptors38DID Code: A Bridge Connecting the Materials Genome Engineering Database with Inheritable Integrated Intelligent Manufacturing11Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches161Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509Optimization of a Heterogeneous Ternary Li3PO4–Li3BO3–Li2SO4 Mixture for Li-Ion Conductivity by Machine Learning62High-throughput screening platform for solid electrolytes combining hierarchical ion-transport prediction algorithms197Recent advances in knowledge discovery for heterogeneous catalysis using machine learning130Machine learning enabled discovery of application dependent design principles for two-dimensional materials18Ni–Co-Based Nanowire Arrays with Hierarchical Core–Shell Structure Electrodes for High-Performance Supercapacitors152020 roadmap on solid-state batteries141Machine learning assisted materials design and discovery for rechargeable batteries466Combining Superionic Conduction and Favorable Decomposition Products in the Crystalline Lithium–Boron–Sulfur System: A New Mechanism for Stabilizing Solid Li-Ion Electrolytes39Structure-property maps with Kernel principal covariates regression44High-throughput production of force-fields for solid-state electrolyte materials20A database of battery materials auto-generated using ChemDataExtractor162Between Liquid and All Solid: A Prospect on Electrolyte Future in Lithium‐Ion Batteries for Electric Vehicles81Ab initio molecular dynamics study of isotope effects in lithium-ion conductors15Rechargeable Battery Electrolytes Capable of Operating over Wide Temperature Windows and Delivering High Safety149A data science approach for advanced solid polymer electrolyte design16Toward design of cation transport in solid-state battery electrolytes: Structure-dynamics relationships50Ultra-fast and accurate binding energy prediction of shuttle effect-suppressive sulfur hosts for lithium-sulfur batteries using machine learning91“More is Different:” Synergistic Effect and Structural Engineering in Double‐Atom Catalysts349Phonon–Ion Interactions: Designing Ion Mobility Based on Lattice Dynamics166First-principles materials simulation and design for alkali and alkaline metal ion batteries accelerated by machine learning7Introduction to lithium-ion battery design2A critical review: Recent advances in “digital” biomolecule detection with single copy sensitivity68Designed high-performance lithium-ion battery electrodes using a novel hybrid model-data driven approach92Machine learning for advanced energy materials208Distilling nanoscale heterogeneity of amorphous silicon using tip-enhanced Raman spectroscopy (TERS) via multiresolution manifold learning60Prediction of the specific heat of polymers from experimental data and machine learning methods51High-throughput screening of ternary vanadate photoanodes for efficient oxygen evolution reactions: A review of band-gap engineering16Visual Analysis on Machine Learning Assisted Prediction of Ionic Conductivity for Solid-State Electrolytes3Designing inorganic electrolytes for solid-state Li-ion batteries: A perspective of LGPS and garnet122Interactions are important: Linking multi-physics mechanisms to the performance and degradation of solid-state batteries92Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors118Fast Ion-Conducting Thioboracite with a Perovskite Topology and Argyrodite-like Lithium Substructure31Machine Learning in Magnetic Materials48First-Principles DFT Study on Inverse Ruddlesden–Popper Tetragonal Compounds as Solid Electrolytes for All-Solid-State Li+-Ion Batteries30Dielectric Constant of Liquid Water Determined with Neural Network Quantum Molecular Dynamics56Predicting and optimizing coupling effect in magnetoelectric multi-phase composites based on machine learning algorithm17Identifying descriptors for Li+ conduction in cubic Li-argyrodites via hierarchically encoding crystal structure and inferring causality80Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning70Automatic Migration Path Exploration for Multivalent Battery Cathodes using Geometrical Descriptors21Structure and Dynamics of Energy Materials from Machine Learning Simulations: A Topical Review†12Database Creation, Visualization, and Statistical Learning for Polymer Li+-Electrolyte Design32Machine Learning Screening of Metal-Ion Battery Electrode Materials116Machine-Learning-Accelerated Catalytic Activity Predictions of Transition Metal Phthalocyanine Dual-Metal-Site Catalysts for CO2 Reduction171Computational discovery of energy materials in the era of big data and machine learning: A critical review60Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale166Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale56Machine Learning Boosting the Development of Advanced Lithium Batteries68High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks138Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening52Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries412Polymer electrolytes and interfaces in solid-state lithium metal batteries408Artificial Intelligence Applied to Battery Research: Hype or Reality?503Two low-expansion Li-ion cathode materials with promising multi-property performance3matExplorer: Visual Exploration on Predicting Ionic Conductivity for Solid-state Electrolytes8Paradigms of frustration in superionic solid electrolytes46

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