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RESEARCH PAPER · Journal of Physics Energy · 2019

Machine learning for the modeling of interfaces in energy storage and conversion materials

Nongnuch Artrith

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42 direct citing works

Machine learning for the modeling of interfaces in energy storage and conversion materialsOriginalHiding in the Crowd: Spectral Signatures of Overcoordinated Hydrogen-Bond Environments26Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide68Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning114Modelling and understanding battery materials with machine-learning-driven atomistic simulations114Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments47Learning What Makes Catalysts Good8Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications82High-dimensional neural network atomic potentials for examining energy materials: some recent simulations39Potential and pH Dependence of the Buried Interface of Membrane-Coated Electrocatalysts4Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning65Computational Modeling of Carbon Dioxide Catalytic Conversion2Applications of Interatomic Potentials Using Neural Network in Materials Science0Four Generations of High-Dimensional Neural Network Potentials873Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations91Open Catalyst 2020 (OC20) Dataset and Community Challenges765Compact atomic descriptors enable accurate predictions via linear models25Machine learning potentials for complex aqueous systems made simple224The Interplay between Diradical Character and Stability in Organic Molecules2AENET–LAMMPS and AENET–TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials29Gaussian Process Regression for Materials and Molecules1221The Role of Water Loading and Germanium Content in Germanosilicate Hydrolysis21Insights into lithium manganese oxide–water interfaces using machine learning potentials50Distributed representations of atoms and materials for machine learning41Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network43Machine‐Learning and Data‐Intensive Methods for Accelerating the Development of Rechargeable Battery Chemistries: A Review6Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li7La3Zr2O12 solid electrolytes38Improvement of the UV light sensors modeling numerical platform performances4Accelerating the theoretical study of Li‐polysulfide adsorption on single‐atom catalysts via machine learning approaches7Modelling Interfaces in Thin-Film Photovoltaic Devices27Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime100Reactive Neural Network Potential for Aluminosilicate Zeolites and Water: Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU5High-Dimensional Neural Network Potential for Liquid Electrolyte Simulations90Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges66Opportunities in the design of metal@oxide core-shell nanoparticles29Representations of Materials for Machine Learning133Enabling automated high-throughput Density Functional Theory studies of amorphous material surface reactions4Modeling ionic conductivity and activation energy in garnet-structured solid electrolytes: The role of composition, grain boundaries and processing16Reactive Neural Network Potential for Aluminosilicate Zeolites and Water: Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU1Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU using reactive neural network potential.6Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey43Prediction of Born effective charges using neural network to study ion migration under electric fields: applications to crystalline and amorphous Li 3 PO 417Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model42New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends40Thermodynamic and kinetic modeling of electrocatalytic reactions using a first-principles approach24Neural Network-Based Simulation Method to Examine Ion Behaviors under Electric Fields: Application to Ion Migration in Amorphous Li3PO40Application of Machine Learning in Energy Storage: A Scientometric Research of a Decade7Enhancing Renewable Energy Storage Conversion Efficiency using ERFE with FFNN0Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations5Metastable grain boundaries: the roles of structural and chemical disorders in their energetics, non-equilibrium kinetic evolution, and mechanical behaviors11Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review38Improving Molecular‐Dynamics Simulations for Solid–Liquid Interfaces with Machine‐Learning Interatomic Potentials12Probing degradation at solid-state battery interfaces using machine-learning interatomic potential20Development of Machine Learning Atomistic Potential for Molecular Simulation of Hematite–Water Interfaces3Generation and correction of machine learning interatomic potential for simulation of liquid metal corrosion with near experimental accuracy: A study for iron corrosion in liquid lead9How to Assess and Predict Electrical Double Layer Properties. Implications for Electrocatalysis226Introduction to machine learning potentials for atomistic simulations35Machine Learning Potentials for Layered IV-VI Materials: Accelerating Accurate Atomic Simulations of SnS for Energy Applications0Defect Segregation, Water Layering, and Proton Transfer at Zirconium Oxynitride/Water Interface Examined Using Neural Network Potential7Atomistic simulation of batteries via machine learning force fields: From bulk to interface16Recent Advances in Organic Phase Change Materials for Thermal Energy Storage: A Review on Sustainable Development Applications51Scalable training of neural network potentials for complex interfaces through data augmentation1Atomistic simulations of oxide–water interfaces using machine learning potentials5Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective6Grand Challenges and Opportunities in Stimulated Dynamic and Resonant Catalysis7Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation4Mechanistic study of mixed lithium halides solid-state electrolytes1Beyond Energies and Forces: Systematic Evaluation of Machine-Learned Potentials for Solid–Liquid Interfaces0

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  1. Hiding in the Crowd: Spectral Signatures of Overcoordinated Hydrogen-Bond Environments

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  2. Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide

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  3. Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning

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    Nongnuch Artrith, Zhexi Lin, Jingguang G. Chen

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  4. Modelling and understanding battery materials with machine-learning-driven atomistic simulations

    Journal of Physics Energy · 2020-08-17

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  5. Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments

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  6. Learning What Makes Catalysts Good

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  7. Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

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  8. High-dimensional neural network atomic potentials for examining energy materials: some recent simulations

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  9. Potential and pH Dependence of the Buried Interface of Membrane-Coated Electrocatalysts

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

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  11. Computational Modeling of Carbon Dioxide Catalytic Conversion

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  12. Applications of Interatomic Potentials Using Neural Network in Materials Science

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  13. Four Generations of High-Dimensional Neural Network Potentials

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  14. Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations

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  15. Open Catalyst 2020 (OC20) Dataset and Community Challenges

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  16. Compact atomic descriptors enable accurate predictions via linear models

    The Journal of Chemical Physics · 2021-06-11

    Claudio Zeni, Kevin Rossi, Aldo Glielmo, Stefano de Gironcoli

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  17. Machine learning potentials for complex aqueous systems made simple

    Spiral (Imperial College London) · 2021-07-27

    Muller, E, Christoph Schran, Fabian L. Thiemann, Rowe, P, Ondřej Maršálek, Angelos Michaelides

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  18. The Interplay between Diradical Character and Stability in Organic Molecules

    Symmetry · 2021-08-07

    Vaska Petakova, Miroslava Nedyalkova, Joanna Stoycheva, Alia Tadjer, Julia Romanova

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  19. AENET–LAMMPS and AENET–TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

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  20. Gaussian Process Regression for Materials and Molecules

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  21. The Role of Water Loading and Germanium Content in Germanosilicate Hydrolysis

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  22. Insights into lithium manganese oxide–water interfaces using machine learning potentials

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  23. Distributed representations of atoms and materials for machine learning

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  24. Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network

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  25. Machine‐Learning and Data‐Intensive Methods for Accelerating the Development of Rechargeable Battery Chemistries: A Review

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  26. Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li7La3Zr2O12 solid electrolytes

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  27. Improvement of the UV light sensors modeling numerical platform performances

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  28. Accelerating the theoretical study of Li‐polysulfide adsorption on single‐atom catalysts via machine learning approaches

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  29. Modelling Interfaces in Thin-Film Photovoltaic Devices

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  30. Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime

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  31. Reactive Neural Network Potential for Aluminosilicate Zeolites and Water: Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU

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  32. High-Dimensional Neural Network Potential for Liquid Electrolyte Simulations

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  33. Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges

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  36. Enabling automated high-throughput Density Functional Theory studies of amorphous material surface reactions

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  37. Modeling ionic conductivity and activation energy in garnet-structured solid electrolytes: The role of composition, grain boundaries and processing

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  38. Reactive Neural Network Potential for Aluminosilicate Zeolites and Water: Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU

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  39. Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU using reactive neural network potential.

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  40. Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

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  41. Prediction of Born effective charges using neural network to study ion migration under electric fields: applications to crystalline and amorphous Li 3 PO 4

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  42. Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model

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  43. New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends

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  45. Neural Network-Based Simulation Method to Examine Ion Behaviors under Electric Fields: Application to Ion Migration in Amorphous Li3PO4

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  46. Application of Machine Learning in Energy Storage: A Scientometric Research of a Decade

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  47. Enhancing Renewable Energy Storage Conversion Efficiency using ERFE with FFNN

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  48. Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations

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  49. Metastable grain boundaries: the roles of structural and chemical disorders in their energetics, non-equilibrium kinetic evolution, and mechanical behaviors

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  50. Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review

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