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RESEARCH PAPER · Electrochimica Acta · 2023

Metal-solvent interaction contribution on voltage for metal ion battery: An interpretable machine learning approach

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10 direct citing papers in this saved network. Explore the next layer with second-order citations.

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42 direct citing works

Metal-solvent interaction contribution on voltage for metal ion battery: An interpretable machine learning approachOriginalUnlocking the Potential of Dual-Ion Batteries: Identifying Polycyclic Aromatic Hydrocarbon Cathodes and Intercalating Salt Combinations through Machine Learning10Machine learning in energy storage material discovery and performance prediction77Integrated Supervised and Unsupervised Machine Learning Approach to Map the Electrochemical Windows Over 4500 Solvents for Battery Applications21Integration of Density Functional Theory and Machine Learning for Electrolyte Optimization in High-Voltage Dual-Ion Battery Design16Braving the Elements: Learning from 60+ Dopants in Battery Materials12Machine‐Learned Modeling for Accelerating Organic Solvent Design in Metal‐Ion Batteries2Unlocking new horizons, challenges of integrating machine learning to energy conversion and storage research9Machine learning aided design of reversible MXene electrocatalysts for Li–air batteries0Density Functional Theory–Machine Learning-Assisted Insightful Identification of Next-Generation High-Voltage Organic Dual-Ion Batteries3Automated Pipeline for High-Throughput Screening of 2D Electrode Materials for Alkali Metal-Ion Batteries1

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  1. Unlocking the Potential of Dual-Ion Batteries: Identifying Polycyclic Aromatic Hydrocarbon Cathodes and Intercalating Salt Combinations through Machine Learning

    ACS Applied Materials & Interfaces · 2023-11-16

    Sandeep Das, Souvik Manna, Biswarup Pathak

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  2. Machine learning in energy storage material discovery and performance prediction

    Chemical Engineering Journal · 2024-05-16

    Guochang Huang, Fuqiang Huang, Wujie Dong

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  3. Integrated Supervised and Unsupervised Machine Learning Approach to Map the Electrochemical Windows Over 4500 Solvents for Battery Applications

    ACS Applied Materials & Interfaces · 2024-07-31

    Souvik Manna, Surya Sekhar Manna, Biswarup Pathak

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  4. Integration of Density Functional Theory and Machine Learning for Electrolyte Optimization in High-Voltage Dual-Ion Battery Design

    ACS Applied Materials & Interfaces · 2024-08-07

    Poulami Paul, Sandeep Das, Souvik Manna, Surya Sekhar Manna, Biswarup Pathak

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  5. Braving the Elements: Learning from 60+ Dopants in Battery Materials

    The Journal of Physical Chemistry C · 2024-09-26

    Eric McCalla

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  6. Machine‐Learned Modeling for Accelerating Organic Solvent Design in Metal‐Ion Batteries

    Advanced Theory and Simulations · 2024-11-19

    Wiwittawin Sukmas, Jiaqian Qin, Rungroj Chanajaree

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  7. Unlocking new horizons, challenges of integrating machine learning to energy conversion and storage research

    Indian Chemical Engineer · 2025-01-09

    Muthuraja Velpandian, Suddhasatwa Basu

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  8. Machine learning aided design of reversible MXene electrocatalysts for Li–air batteries

    Journal of Materials Chemistry A · 2026-01-01

    Sandeep Das, Souvik Manna, Amitabha Das, Brindaban Modak, Biswarup Pathak

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  9. Density Functional Theory–Machine Learning-Assisted Insightful Identification of Next-Generation High-Voltage Organic Dual-Ion Batteries

    Chemistry of Materials · 2026-02-11

    Poulami Paul, Souvik Manna, Sandeep Das, Biswarup Pathak

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  10. Automated Pipeline for High-Throughput Screening of 2D Electrode Materials for Alkali Metal-Ion Batteries

    ACS Materials Letters · 2026-02-25

    Souvik Manna, Biswarup Pathak

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