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RESEARCH PAPER · Physical Chemistry Chemical Physics · 2017

Materials space of solid-state electrolytes: unraveling chemical composition–structure–ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches

Natalia V. Kireeva, Vladislav S. Pervov

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Materials space of solid-state electrolytes: unraveling chemical composition–structure–ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approachesOriginalApplying machine learning to the design of materials for lithium ion battery3The dye-sensitized solar cell database71Applying machine learning to accelerate new materials development22Machine learning for renewable energy materials312Sur-/interfacial regulation in all-solid-state rechargeable Li-ion batteries based on inorganic solid-state electrolytes: advances and perspectives78An investigation of the structural properties of Li and Na fast ion conductors using high-throughput bond-valence calculations and machine learning72Fast Ionic Conductivity in the Most Lithium-Rich Phosphidosilicate Li14SiP668Building Better Batteries in the Solid State: A Review277A Critical Review of Machine Learning of Energy Materials606Materials Informatics Screening of Li‐Rich Layered Oxide Cathode Materials with Enhanced Characteristics Using Synthesis Data29Machine learning: Accelerating materials development for energy storage and conversion370NIR luminescence features of Nd3+ ion in lithium antimonite glass system8A data science approach for advanced solid polymer electrolyte design16Machine learning for advanced energy materials208Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors118Machine learning analysis of microwave dielectric properties for seven structure types: The role of the processing and composition19Machine Learning Screening of Metal-Ion Battery Electrode Materials116Computational discovery of energy materials in the era of big data and machine learning: A critical review60Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions28Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries412Artificial Intelligence Applied to Battery Research: Hype or Reality?503Recent machine learning guided material research - A review12Capacity prediction of K-ion batteries: a machine learning based approach for high throughput screening of electrode materials30Machine learning reveals factors that control ion mobility in anti-perovskite solid electrolytes32Silicate Minerals1Evaluation of performance of machine learning methods in mining structure–property data of halide perovskite materials21Accelerating the discovery of battery electrode materials through data mining and deep learning models30Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology24Machine learning in energy chemistry: introduction, challenges and perspectives31Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators160Advances in machine learning- and artificial intelligence-assisted material design of steels109Global Mapping of Structures and Properties of Crystal Materials7Modeling ionic conductivity and activation energy in garnet-structured solid electrolytes: The role of composition, grain boundaries and processing16Computational materials discovery and development for Li and non-Li advanced battery chemistries8Machine learning-based evaluation of functional characteristics of Li-rich layered oxide cathode materials using the data of XPS and XRD spectra16Mining ionic conductivity descriptors of antiperovskite electrolytes for all-solid-state batteries via machine learning27Ion Mobility in Crystalline Battery Materials63Machine Learning As a Tool to Accelerate the Search for New Materials for Metal-Ion Batteries1Progress of machine learning in materials design for Li-Ion battery19Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes1Machine learning in the era of smart automation for renewable energy materials9Assessing Correlations between Phonon Features and Cation Migration Barriers in Multivalent Solid Electrolytes8Data refinement for enhanced ionic conductivity prediction in garnet-type solid-state electrolytes12Electro‐Chemo‐Mechanical Degradation in Solid‐State Batteries: A Review of Microscale and Multiphysics Modeling59Accelerated discovery of novel inorganic solid-state electrolytes through machine learning-assisted hierarchical screening20Recent trends of machine learning on energy storage devices7A review of machine learning applications in polymer composites: advancements, challenges, and future prospects135Accelerating discovery and design of high-performance solid-state electrolytes: a machine learning approach13Artificial intelligence technology in materials selection, device engineering and parameter optimisation for triboelectric nanogenerator4Recent development in sodium metal batteries: challenges, progress, and perspective40Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency44Applications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries57Ga-stabilized LLZO: A comprehensive review of structural stability, ionic conductivity, and co-doping strategies0

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