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RESEARCH PAPER · Journal of Physics Communications · 2020

Machine learning-assisted cross-domain prediction of ionic conductivity in sodium and lithium-based superionic conductors using facile descriptors

Yijie Xu, Yun Zong, Kedar Hippalgaonkar

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Machine learning-assisted cross-domain prediction of ionic conductivity in sodium and lithium-based superionic conductors using facile descriptorsOriginalMachine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors118Detection Method on Data Accuracy Incorporating Materials Domain Knowledge24Automatically Capturing Key Features for Predicting Superionic Conductivity of Solid-State Electrolytes Using a Neural Network9Feature selection method reducing correlations among features by embedding domain knowledge101An automatic descriptors recognizer customized for materials science literature36High‐Throughput Data‐Driven Prediction of Stable High‐Performance Na‐Ion Sulfide Solid Electrolytes24Basic guidelines of first-principles calculations for suitable selection of electrochemical Li storage materials: a review38Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges66Model-based evaluation and data requirements for parallel kinetic experimentation and data-driven reaction identification and optimization13Exploring the Possibility of Machine Learning for Predicting Ionic Conductivity of Solid-State Electrolytes54Influence of substitutions on the structure, ionic conductivity, and phase transitions in the system of Na3Fe2(1-x)Sc2x(PO4)3 (0≤x≤0.06) solid solutions3Screening Platform for Promising Na Superionic Conductors for Na-Ion Solid-State Electrolytes23Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries via Machine Learning37Machine learning for beyond Li-ion batteries: Powering the research39Revolutionizing Solid‐State NASICON Sodium Batteries: Enhanced Ionic Conductivity Estimation through Multivariate Experimental Parameters Leveraging Machine Learning16Mining ionic conductivity descriptors of antiperovskite electrolytes for all-solid-state batteries via machine learning27Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries93Developing Machine Learning Models for Predicting Multiple Physical Properties of Ionic Liquids through a Combined Constitution-Structure-Interaction Descriptor7A comprehensive data network for data-driven study of battery materials9Deep learning-driven evaluation and prediction of ion-doped NASICON materials for enhanced solid-state battery performance11Mechanical issues in sulfide-based all-solid-state batteries: Origin, monitoring, and intelligent analysis16Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook31Structural, morphological, and electric study of doped- Na2Zn2TeO6 family in a wide range of temperatures4Machine learning for a sustainable energy future15Design of sodium superionic conductors based on multiple crystal structure prediction methods3Machine-learning-assisted discovery of lattice dynamics signatures of sodium superionic conductors5Computational understanding and multiscale simulation of secondary batteries14Descriptors Divide‐and‐Conquer Enables Multifaceted and Interpretable Materials Structure–Activity Relationship Analysis24Application-oriented design of machine learning paradigms for battery science69Domain knowledge-assisted materials data anomaly detection towards constructing high-performance machine learning models35Ion transport mechanisms in solid electrolytes assisted by machine learning0Machine learning pipelines for the design of solid-state electrolytes30Applications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries57AI-driven exploration and design of inorganic battery materials14Machine learning in conductivity development of specific alloys1Structure divide-and-conquer: dual graph representation for accurate ionic transport barrier prediction of inorganic compounds3LiClustCondAI: A clustering-based ensemble regression framework for ionic conductivity predictions in solid-state electrolytes0The role and strategies of data-driven machine learning in design optimization and performance evaluation of electrochemical energy storage materials0

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  2. Detection Method on Data Accuracy Incorporating Materials Domain Knowledge

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  3. Automatically Capturing Key Features for Predicting Superionic Conductivity of Solid-State Electrolytes Using a Neural Network

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  4. Feature selection method reducing correlations among features by embedding domain knowledge

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  10. Exploring the Possibility of Machine Learning for Predicting Ionic Conductivity of Solid-State Electrolytes

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  11. Influence of substitutions on the structure, ionic conductivity, and phase transitions in the system of Na3Fe2(1-x)Sc2x(PO4)3 (0≤x≤0.06) solid solutions

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  12. Screening Platform for Promising Na Superionic Conductors for Na-Ion Solid-State Electrolytes

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  13. Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries via Machine Learning

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  15. Revolutionizing Solid‐State NASICON Sodium Batteries: Enhanced Ionic Conductivity Estimation through Multivariate Experimental Parameters Leveraging Machine Learning

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  16. Mining ionic conductivity descriptors of antiperovskite electrolytes for all-solid-state batteries via machine learning

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  18. Developing Machine Learning Models for Predicting Multiple Physical Properties of Ionic Liquids through a Combined Constitution-Structure-Interaction Descriptor

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  20. Deep learning-driven evaluation and prediction of ion-doped NASICON materials for enhanced solid-state battery performance

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  25. Design of sodium superionic conductors based on multiple crystal structure prediction methods

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