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RESEARCH PAPER · Energy storage materials · 2019

A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning

Arghya Bhowmik, Ivano E. Castelli, Juan María García-Lastra, Peter Bjørn Jørgensen, Ole Winther, Tejs Vegge

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A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learningOriginalAnterior segment photography.1MoS 2 /carbon composites prepared by ball-milling and pyrolysis for the high-rate and stable anode of lithium ion capacitors29Descriptors for Electrolyte-Renormalized Oxidative Stability of Solvents in Lithium-Ion Batteries31Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies153Battery 2030+ Roadmap46Machine-enabled inverse design of inorganic solid materials: promises and challenges190Reactivity and Catalysis by Nanoalloys5Artificial intelligence and machine learning for targeted energy storage solutions64AI Fast Track to Battery Fast Charge29Fossil Energy Workshop on Quantum Information Science & Technology (Summary Report)6Machine learning assisted materials design and discovery for rechargeable batteries466Modeling the chemo-mechanical behavior of all-solid-state batteries: a review.82An accurate machine-learning calculator for optimization of Li-ion battery cathodes82Sulfide and Oxide Inorganic Solid Electrolytes for All-Solid-State Li Batteries: A Review331Carbon materials for high-performance potassium-ion energy-storage devices45Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials55Retracted: Optimal peak shifting of a domestic load connected to utility grid using storage battery based on deep Q‐learning network10The future of sustainable chemistry and process: Convergence of artificial intelligence, data and hardware34Leveraging Cation Identity to Engineer Solid Electrolyte Interphases for Rechargeable Lithium Metal Anodes21An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data68Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning186Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning65Generative deep learning as a tool for inverse design of high entropy refractory alloys50Monte Carlo assisted sensitivity analysis of a Li-ion battery with a phase change material15Room‐Temperature Sodium–Sulfur Batteries and Beyond: Realizing Practical High Energy Systems through Anode, Cathode, and Electrolyte Engineering193Rapid Interfacial Exchange of Li Ions Dictates High Coulombic Efficiency in Li Metal Anodes91Current and future lithium-ion battery manufacturing763Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches72Tomographic reconstruction of oxygen orbitals in lithium-rich battery materials92Progress and perspective of the cathode/electrolyte interface construction in all‐solid‐state lithium batteries124Kinetic Monte Carlo simulations applied to Li-ion and post Li-ion batteries: a key link in the multi-scale chain48Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage260Data Management Plans: the Importance of Data Management in the BIG‐MAP Project**50Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries412Artificial Intelligence Applied to Battery Research: Hype or Reality?503A Systematic Review of Deep Learning Approaches for Natural Language Processing in Battery Materials Domain9Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes139Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries65Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks55Review—Reference Electrodes in Li-Ion and Next Generation Batteries: Correct Potential Assessment, Applications and Practices64Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective376Towards autonomous high-throughput multiscale modelling of battery interfaces33Learning the laws of lithium-ion transport in electrolytes using symbolic regression25Robotic cell assembly to accelerate battery research46Three-dimensional modeling of Lithium-ion battery electrode manufacturing0A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+235Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?87Intelligent disassembly of electric-vehicle batteries: a forward-looking overview172Unsupervised machine learning to classify crystal structures according to their structural distortion: A case study on Li-argyrodite solid-state electrolytes11Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review93Theory-guided experimental design in battery materials research156Thermodynamic Analysis of Initial Steps for Void Formation at Lithium/Solid Electrolyte Interphase Interfaces21From materials discovery to system optimization by integrating combinatorial electrochemistry and data science37New engineering science insights into the electrodes pairing of electrochemical energy storage devices assisted by machine learning0Advances in Carbon Materials for Sodium and Potassium Storage177Gaussian process sensitivity analysis of a capacityfade model for lithium-ion batteries0A Generative Approach to Materials Discovery, Design, and Optimization121Machine learning-facilitated multiscale imaging for energy materials27Brokering between tenants for an international materials acceleration platform5Toward a Li‐Ion Battery Ontology Covering Production and Material Structure21Sensitivity analysis methodology for battery degradation models34Towards Sustainable Fuel Cells and Batteries with an AI Perspective51State of health estimation of lithium-ion battery with automatic feature extraction and self-attention learning mechanism98Artificial Intelligence in the Service of Dynamical Systems Studies0Designing strategies of advanced electrode materials for high-rate rechargeable batteries44Machine learning-inspired battery material innovation49Materials funnel 2.0 – data-driven hierarchical search for exploration of vast chemical spaces4Solid State Ionics 2011–2021: Trends and Prospects1Solid State Ionics 2011–2021: Trends and Prospects0Lithium Batteries and the Solid Electrolyte Interphase (SEI)—Progress and Outlook830Intelligent Demand and Supply of Electrical Vehicles0Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators160Toward Improving the Thermal Stability of Negative Electrode Materials: Differential Scanning Calorimetry and In Situ High-Temperature X-ray Diffraction/X-ray Absorption Spectroscopy Studies of Li2ZnTi3O8 and Related Compounds9Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Data-driven multiscale simulation of solid-state batteries via machine learning27Materials cartography: A forward-looking perspective on materials representation and devising better maps13An Overview on Transport Phenomena within Solid Electrolyte Interphase and Their Impact on the Performance and Durability of Lithium-Ion Batteries27A fuzzy classification framework to identify equivalent atoms in complex materials and molecules5Enhancing ReaxFF for Molecular Dynamics Simulations of Lithium-Ion Batteries: An interactive reparameterization protocol0Brokering between tenants for an international materials acceleration platform46Multiscale Modelling Methodologies of Lithium-Ion Battery Aging: A Review of Most Recent Developments48Data‐Driven Virtual Material Analysis and Synthesis for Solid Electrolyte Interphases19Bridging the Gap: Electrode Microstructure and Interphase Characterization by Combining ToF‐SIMS and Machine Learning18Designing electrodes and electrolytes for batteries by leveraging deep learning26A critical review of future aspects of digitalization next generation Li-ion batteries manufacturing process38Advancing next-generation proton-exchange membrane fuel cell development in multi-physics transfer204Introduction: Battery Materials: Bringing It All Together for Tomorrow’s Energy Storage Needs0Na vs. Li metal anodes for batteries: unraveling thermodynamic and electronic origins of voids and developing descriptors for artificial surface coatings4Enhancing ReaxFF for molecular dynamics simulations of lithium-ion batteries: an interactive reparameterization protocol18Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects55Nonlinear Potentiodynamic Battery Charging Protocols for Fun, Education, and Application4AI-assisted inverse design of sequence-ordered high intrinsic thermal conductivity polymers33Multiscale modeling for enhanced battery health analysis: Pathways to longevity45Autonomous battery optimisation by deploying distributed experiments and simulations2Machine Learning Strategies for Forecasting Discharge Capacity in Lithium-Ion Batteries with NCM Layered Cathode Material1Energy management strategies in distribution system integrating electric vehicle and battery energy storage system: A review40AI for science in electrochemical energy storage: A multiscale systems perspective on transportation electrification18Comparative Analysis of Traditional And AI Based Fault Detection for Battery Thermal Management System2Molecular relaxation by reverse diffusion with time step prediction6Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends56Autonomous Battery Optimization by Deploying Distributed Experiments and Simulations19Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway0Ceramic Electrolytes1DFT on the material design and understanding for energy conversion and storage: Present state and further perspectives0Investigating Electrical Systems with Artificial Intelligence0Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces19Leveraging machine learning for accelerated materials innovation in lithium-ion battery: A review54Deep Learning-Driven Molecular Generation and Electrochemical Property Prediction for Optimal Electrolyte Additive Design8A dual dimensionality reduction and feature weighting integration transfer learning method for lithium-ion battery state of health estimation8Advancing chemical engineering technology with artificial intelligence21Artificial intelligence for science in quantum, atomistic, and continuum systems71Prototyping with Generative AI Design to Debottleneck Conventional Design: Incorporation on Proton Exchange Membrane Electrolyzer2Generative Deep Learning for Advanced Battery Materials3AI‐Driven Big Data Frameworks for Electrode–Electrolyte Interphases in Batteries28Application of machine learning to rechargeable aqueous zinc-ion batteries: Advancements and prospects3Machine Learning‐Based Inverse Design for Functional Materials: Methods, Challenges, and Engineering Applications19Comparing fine-tuning strategies for machine learning force fields in lithium-ion diffusion0Unlocking the Chemical Space for Rechargeable Batteries with a Generative Solvent Design System3Unlocking rechargeable battery materials with artificial intelligence: From data-descriptor-model workflows to closed-loop discovery0Machine Learning Interatomic Potentials for Modeling Solid-State Batteries0

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  1. Anterior segment photography.

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  2. MoS 2 /carbon composites prepared by ball-milling and pyrolysis for the high-rate and stable anode of lithium ion capacitors

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  3. Descriptors for Electrolyte-Renormalized Oxidative Stability of Solvents in Lithium-Ion Batteries

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  4. Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies

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  6. Machine-enabled inverse design of inorganic solid materials: promises and challenges

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  7. Reactivity and Catalysis by Nanoalloys

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  9. AI Fast Track to Battery Fast Charge

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  10. Fossil Energy Workshop on Quantum Information Science & Technology (Summary Report)

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  12. Modeling the chemo-mechanical behavior of all-solid-state batteries: a review.

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  14. Sulfide and Oxide Inorganic Solid Electrolytes for All-Solid-State Li Batteries: A Review

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  15. Carbon materials for high-performance potassium-ion energy-storage devices

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  17. Retracted: Optimal peak shifting of a domestic load connected to utility grid using storage battery based on deep Q‐learning network

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  19. Leveraging Cation Identity to Engineer Solid Electrolyte Interphases for Rechargeable Lithium Metal Anodes

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  20. An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data

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  21. Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning

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  22. Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

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  23. Generative deep learning as a tool for inverse design of high entropy refractory alloys

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  24. Monte Carlo assisted sensitivity analysis of a Li-ion battery with a phase change material

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  29. Tomographic reconstruction of oxygen orbitals in lithium-rich battery materials

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  38. Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

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  45. Three-dimensional modeling of Lithium-ion battery electrode manufacturing

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  46. A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+

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  47. Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

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