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RESEARCH PAPER · Journal of Materials Chemistry A · 2020

Bayesian-optimization-guided experimental search of NASICON-type solid electrolytes for all-solid-state Li-ion batteries

Maho Harada, Hayami Takeda, Shinya Suzuki, K. Nakano, Naoto Tanibata, Masanobu Nakayama, Masayuki Karasuyama, Ichiro Takeuchi

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Bayesian-optimization-guided experimental search of NASICON-type solid electrolytes for all-solid-state Li-ion batteriesOriginalEfficient Experimental Search for Discovering a Fast Li-Ion Conductor from a Perovskite-Type LixLa(1–x)/3NbO3 (LLNO) Solid-State Electrolyte Using Bayesian Optimization31Materials Optimization for All-solid-state Li Secondary Batteries by Materials Informatics0Efficient sampling for decision making in materials discovery*10Electrolyte recommender system for batteries using ensemble Bayesian optimization8Materials informatics for discovery of ion conductive ceramics for batteries11Bayesian optimization for goal-oriented multi-objective inverse material design36Applied Machine Learning for Developing Next‐Generation Functional Materials72Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models44Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?163Molecular Dynamics Simulation of Li-Ion Conduction at Grain Boundaries in NASICON-Type LiZr 2 (PO 4 ) 3 Solid Electrolytes31The Role of Sintering Temperature and Dual Metal Substitutions (Al3+, Ti4+) in the Development of NASICON-Structured Electrolyte3Bayesian optimisation with transfer learning for NASICON-type solid electrolytes for all-solid-state Li-metal batteries18Na superionic conductor-type LiZr 2 (PO 4 ) 3 as a promising solid electrolyte for use in all-solid-state Li metal batteries19Prediction of the photoelectrochemical performance of hematite electrodes using analytical data21Process optimisation for NASICON-type solid electrolyte synthesis using a combination of experiments and bayesian optimisation31Density Functional Theory Studies on Li Metal Electrode/Garnet‐Type Li7La3Zr2O12 Solid Electrolyte Interfaces for Application in All‐Solid‐State Batteries12Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate24A review of the recent progress in battery informatics178Gaussian process-based prognostics of lithium-ion batteries and design optimization of cathode active materials32Comparison of conceptually different multi-objective Bayesian optimization methods for material design problems28Tuning of Bayesian optimization for materials synthesis: simulation of the one-dimensional case13Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells for Electric Vehicle Operational Scenarios13Finding a novel electrolyte solution of lithium-ion batteries using an autonomous search system based on ensemble optimization16Recent development in the field of ceramics solid-state electrolytes: I—oxide ceramic solid-state electrolytes52Influence of AlPO4 Impurity on the Electrochemical Properties of NASICON‐Type Li1.5Al0.5Ti1.5(PO4)3 Solid Electrolyte24Next-generation battery technology based on solid-state electrolytes3Data‐Driven Approach for Rational Synthesis of Zeolites and Other Nanoporous Materials2Tuning Bayesian optimization for materials synthesis: simulating two- and three-dimensional cases10Li-ion transport studies of NASICON-type LiZr2(PO4)3 solid electrolyte crystallizing in rhombohedral structure at room temperature11Solid Electrolytes Based on NASICON-Structured Phosphates for Lithium Metal Batteries36Multiobjective Solid Electrolyte Design of Tetragonal and Cubic Inverse-Perovskites for All-Solid-State Lithium-Ion Batteries by High-Throughput Density Functional Theory Calculations and AI-Driven Methods13Machine learning based analysis of metal support co-sintering process for solid oxide fuel cells19Active Learning Platform for Accelerating the Search for High-Voltage Cathode Materials in an Extensive Chemical Space4Charge–discharge properties of LiMn 2 O 4 -group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models0In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Materials Informatics for Thermistor Properties of Mn–Co–Ni Oxides14A robust methodology for PEC performance analysis of photoanodes using machine learning and analytical data13Computational Study on the Li Metal Negative Electrode and Inorganic Solid Electrolyte Interface0Effect of synthesis process on the Li-ion conductivity of LiTa 2 PO 8 solid electrolyte materials for all-solid-state batteries9Na[Mn 0.36 Ni 0.44 Ti 0.15 Fe 0.05 ]O 2 predicted via machine learning for high energy Na-ion batteries15Proton conductivity of Li + –H + exchanged Li 7 La 3 Zr 2 O 12 dense membranes prepared by molten long-chain saturated fatty acids6Chloride electrode composed of ubiquitous elements for high-energy-density all-solid-state sodium-ion batteries18Advanced Integrated Fast-Charging Protocol for Lithium-Ion Batteries by Considering Degradation17NaSICON-type materials for lithium-ion battery applications: Progress and challenges67Solid-state electrolytes: a way to increase the power of lithium-ion batteries27Subtraction Descriptors in Machine Learning for Optimizing the Cocatalyst Effect of Cobalt Phosphate on Hematite Photoanodes5Bayesian-optimization-assisted efficient operation for direct ammonia solid oxide fuel cells12Predicting Anion Redox in Secondary Battery Cathode Materials with a Data-Driven Model4Prediction of Li-ion conductivity in Ca and Si co-doped LiZr2(PO4)3 using a denoising autoencoder for experimental data1Sparse modeling based Bayesian optimization for experimental design3Rare Earth Metal Ion‐Doped Halide Solid Electrolytes plus Ta5+ Substitution for Long Cycling All‐Solid‐State Batteries56Experimental data-driven efficient exploration of the composition and process conditions of Li-rich NASICON-type solid electrolytes4Enhancing structural integrity and long-term cycling stability of high-voltage single-crystalline Ni-rich cathodes via surface/subsurface dual-functional modification engineering26Multi‐Objective Optimization of Ionic Polymer Electrolytes for High‐Voltage Fast‐Charging and Versatile Lithium Batteries23Machine-learning-based efficient parameter space exploration for energy storage systems9Ternary Potassium Single Cation Ionic Liquids Explored by Bayesian Optimization1Enhancement of Li-Ion Battery Cathode Performance via Bayesian and Genetic Algorithm Optimization: Experimental and Numerical Analysis1Machine learning pipelines for the design of solid-state electrolytes30Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries23Deep learning based SEM image analysis for predicting ionic conductivity in LiZr 2 (PO 4 ) 3 -based solid electrolytes0Deconstruction Analysis and Regeneration Optimization of Solid Electrolyte Components for Energy Storage Based on the Finite Data Statistics0Accelerated Discovery of High Performance Ni 3 S 4 /Ni 3 Mo HER Catalysts via Bayesian Optimization7Small-data machine learning for resolving degradation challenges in energy devices2From printed architectures to functional batteries: printing technologies, post-processing, and sintering9Electrolyte engineering for lithium–sulfur batteries: Recent progress and perspectives2Optimize Before You Synthesize—Enhancing the Ionic Conductivity of Li 7 SiPS 8 Using Bayesian Optimization0Optimize Before You Synthesize—Enhancing the Ionic Conductivity of Li 7 SiPS 8 Using Bayesian Optimization0Improving Ionic Conductivity in NASICON-Type Solid Electrolytes through Suppression of Lithium Volatilization1Bridging chemistry and composition-space exploration: Particle swarm optimization of multicomponent glassy sulfide electrolytes0Progress on Halide Solid State Electrolytes: Structural Regulation, Interfacial Engineering, and Machine Learning Driven Paradigms2Artificial intelligence-assisted investigation of ion transport mechanisms and material screening for solid-state electrolytes0Cost-aware sequential Bayesian optimal experimental design for lithium-ion battery life testing0

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  1. Efficient Experimental Search for Discovering a Fast Li-Ion Conductor from a Perovskite-Type LixLa(1–x)/3NbO3 (LLNO) Solid-State Electrolyte Using Bayesian Optimization

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  2. Materials Optimization for All-solid-state Li Secondary Batteries by Materials Informatics

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  3. Efficient sampling for decision making in materials discovery*

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  4. Electrolyte recommender system for batteries using ensemble Bayesian optimization

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  5. Materials informatics for discovery of ion conductive ceramics for batteries

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  6. Bayesian optimization for goal-oriented multi-objective inverse material design

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  7. Applied Machine Learning for Developing Next‐Generation Functional Materials

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  8. Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models

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  9. Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?

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  10. Molecular Dynamics Simulation of Li-Ion Conduction at Grain Boundaries in NASICON-Type LiZr 2 (PO 4 ) 3 Solid Electrolytes

    The Journal of Physical Chemistry C · 2021-10-25

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  11. The Role of Sintering Temperature and Dual Metal Substitutions (Al3+, Ti4+) in the Development of NASICON-Structured Electrolyte

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    Hashlina Rusdi, Roshidah Rusdi, Shujahadeen Bakr Aziz, Abdullah Saad Alsubaie, Khaled Hussein Mahmoud, Mohd F. Z. Kadir

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  12. Bayesian optimisation with transfer learning for NASICON-type solid electrolytes for all-solid-state Li-metal batteries

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  13. Na superionic conductor-type LiZr 2 (PO 4 ) 3 as a promising solid electrolyte for use in all-solid-state Li metal batteries

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  14. Prediction of the photoelectrochemical performance of hematite electrodes using analytical data

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  15. Process optimisation for NASICON-type solid electrolyte synthesis using a combination of experiments and bayesian optimisation

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  16. Density Functional Theory Studies on Li Metal Electrode/Garnet‐Type Li7La3Zr2O12 Solid Electrolyte Interfaces for Application in All‐Solid‐State Batteries

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  17. Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate

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  18. A review of the recent progress in battery informatics

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  19. Gaussian process-based prognostics of lithium-ion batteries and design optimization of cathode active materials

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  20. Comparison of conceptually different multi-objective Bayesian optimization methods for material design problems

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  21. Tuning of Bayesian optimization for materials synthesis: simulation of the one-dimensional case

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  22. Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells for Electric Vehicle Operational Scenarios

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  23. Finding a novel electrolyte solution of lithium-ion batteries using an autonomous search system based on ensemble optimization

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  24. Recent development in the field of ceramics solid-state electrolytes: I—oxide ceramic solid-state electrolytes

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  25. Influence of AlPO4 Impurity on the Electrochemical Properties of NASICON‐Type Li1.5Al0.5Ti1.5(PO4)3 Solid Electrolyte

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  26. Next-generation battery technology based on solid-state electrolytes

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  29. Li-ion transport studies of NASICON-type LiZr2(PO4)3 solid electrolyte crystallizing in rhombohedral structure at room temperature

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  34. Charge–discharge properties of LiMn 2 O 4 -group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models

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