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RESEARCH PAPER · Chemistry of Materials · 2024

Multimodal Machine Learning for Materials Science: Discovery of Novel Li-Ion Solid Electrolytes

Shuo Wang, Sheng Rong Gong, Thorben Böger, Jon A. Newnham, Daniele Vivona, Sokseiha Muy, Kiarash Gordiz, Abhishek Aggarwal, Taishan Zhu, Wolfgang G. Zeier, Jeffrey C. Grossman, Yang Shao‐Horn

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Multimodal Machine Learning for Materials Science: Discovery of Novel Li-Ion Solid ElectrolytesOriginalClassification of (dis)ordered structures as superionic lithium conductors with an experimental structure–conductivity database7High-throughput design of bimetallic materials via multimodal machine learning and the accessibility index5Machine-learning-aided screening of inorganic lithium solid-state electrolytes with a wide electrochemical window7Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials68How Machine Learning Has Driven the Development of Rechargeable Ion Batteries21Covalent organic framework-based solid polymer electrolytes for metal-ion batteries: pioneering the future of DFT, MD, and ML techniques4Data Fusion of Deep Learned Molecular Embeddings for Property Prediction3Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries23Deep Learning-Driven Integration of Multimodal Data for Material Property Predictions3Large-Scale Meta-Analysis of Nanomaterials Toxicity Based on Natural Language Processing of Scientific Articles7AI agents for solid electrolytes: opportunities, challenges, and future directions14AI for battery-accelerated discovery of high-voltage electrolytes for advanced lithium batteries0Multimodal Machine Learning for Data-Driven Materials Selection: A Review of Foundations, Advances, and Future Directions0Artificial Intelligence in Accelerating Materials Discovery: Opportunities and Challenges9Data-driven paradigms for advancing alkali-metal-ion battery technologies0Motif-to-spectrum learning enables interpretable discovery and inverse design of optical absorbers0Integrating physical descriptors into crystal graph convolution neural networks for designing 2D materials with large exciton binding energies0Materials Informatics in Carbon Materials for Energy and Sustainability0Leveraging Machine Learning for Accelerated Electrode–Electrolyte Interface Design in Rechargeable Li‐Based Batteries0Rechargeable batteries for electrochemical energy storage: Review of materials challenges and prospects0Multimodal and cross-modal learning techniques0Machine Learning of Band Gaps and Thermodynamic Stability in A2BB′X6 (X = F, Cl, Br, I) Double Perovskites with Uncertainty-Aware Multi-Objective Screening0

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  1. Classification of (dis)ordered structures as superionic lithium conductors with an experimental structure–conductivity database

    Digital Discovery · 2025-01-01

    Daniel B. McHaffie, Zachery W. B. Iton, Jadon M. Bienz, Forrest A. L. Laskowski, Kimberly A. See

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  2. High-throughput design of bimetallic materials via multimodal machine learning and the accessibility index

    Chemical Science · 2025-01-01

    Yuming Gu, Yating Gu, Maochen Yang, Shisi Tang, Jiawei Chen, Xinyi Liang, Zheng Dong, Zekun Li, Fengqi Song, Yang Gao, Yan Zhu, Yinghuan Shi, Jing Ma

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  3. Machine-learning-aided screening of inorganic lithium solid-state electrolytes with a wide electrochemical window

    Journal of Materials Chemistry A · 2025-01-01

    Jiajing Chen, Jiang Lu, Shendong Tan, Jun Yang, Zihui Li, Chen Bai, Xiang Zhang, Rongao Li, Yaoshu Xie, Ming Liu, Yan‐Bing He, Tingzheng Hou

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  4. Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials

    Advanced Functional Materials · 2025-05-13

    Xue Feng Jiang, Huadong Fu, Yang Bai, Lei Jiang, Hongtao Zhang, Weiren Wang, Peiwen Yun, Jingjin He, Dezhen Xue, Turab Lookman, Yanjing Su, Jianxin Xie

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  5. How Machine Learning Has Driven the Development of Rechargeable Ion Batteries

    Advanced Energy Materials · 2025-09-09

    Huawei Liu, Sihui Li, Shan Zhu, Yin Hu, Xiaopeng Han, Chunsheng Shi, Fang He, He Chunnian, Biao Chen, Naiqin Zhao

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  6. Covalent organic framework-based solid polymer electrolytes for metal-ion batteries: pioneering the future of DFT, MD, and ML techniques

    Energy storage materials · 2025-10-01

    Sadegh Kaviani

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  7. Data Fusion of Deep Learned Molecular Embeddings for Property Prediction

    Journal of Chemical Information and Modeling · 2025-10-27

    Robert J. Appleton, Brian C. Barnes, Alejandro H. Strachan

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  8. Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries

    Science Advances · 2025-11-26

    Zhilong Wang, Wolfgang G. Zeier, Fengqi You

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  9. Deep Learning-Driven Integration of Multimodal Data for Material Property Predictions

    Computation · 2025-12-01

    Valter Costa, José Manuel Oliveira, Patrícia Ramos

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  10. Large-Scale Meta-Analysis of Nanomaterials Toxicity Based on Natural Language Processing of Scientific Articles

    ACS Applied Nano Materials · 2025-12-23

    Amauri Jardim de Paula, Romana Petry, James M. Almeida, Andre Caetano, José Sales, Odair P. Ferreira, Diego S. T. Martinez, Henry Julio Kobs, Andréia Fonseca de Faria

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  11. AI agents for solid electrolytes: opportunities, challenges, and future directions

    AI Agent · 2025-12-31

    Qian Wang, Ryuhei Sato, Regina García-Méndez, Woosun Jang, Pengfei Ou, Aloysius Soon, Jie Zhao, Xiaonan Wang, Shin-ichi Orimo, Eric Jianfeng Cheng

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  12. AI for battery-accelerated discovery of high-voltage electrolytes for advanced lithium batteries

    Chemical Society Reviews · 2026-01-01

    Yu Zhong Zhan, Nan Chen, Li Li, Feng Wu, Renjie Chen

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  13. Multimodal Machine Learning for Data-Driven Materials Selection: A Review of Foundations, Advances, and Future Directions

    IEEE Access · 2026-01-01

    Yuwei Zhang, Chou Yong Tan, Bo Wang, Yew Hoong Wong

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  14. Artificial Intelligence in Accelerating Materials Discovery: Opportunities and Challenges

    ChemistrySelect · 2026-02-01

    Avadhesh Kumar Yadav

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  15. Data-driven paradigms for advancing alkali-metal-ion battery technologies

    Energy storage materials · 2026-03-15

    Hengjia Shao, Naveen Kumar, Wei-Hong Lai, Hao Li, Xue Jia, Hua-Kun Liu, Yun-Xiao Wang

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  16. Motif-to-spectrum learning enables interpretable discovery and inverse design of optical absorbers

    Matter & Light · 2026-05-01

    Xiaoyu Yang, Yuanhui Sun, Lei Shen, Lijun Zhang

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  17. Integrating physical descriptors into crystal graph convolution neural networks for designing 2D materials with large exciton binding energies

    Computational Materials Today · 2026-05-30

    S Liu, Z.H. Liu, Xin Liu, Yuanhui Sun, Lijun Zhang

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  18. Materials Informatics in Carbon Materials for Energy and Sustainability

    Advanced Energy and Sustainability Research · 2026-07-01

    Koki Fujita, Shunsuke Shimizu, Shiho Mukaida, Wei W. Yu, Takeharu Yoshii, Hirotomo Nishihara

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  19. Leveraging Machine Learning for Accelerated Electrode–Electrolyte Interface Design in Rechargeable Li‐Based Batteries

    Small · 2026-08-14

    Xiaorui Liu, Qingyu Li, Jianghao Liang, Zhiqiang Li, Haozhi Wang, Yida Deng

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  20. Rechargeable batteries for electrochemical energy storage: Review of materials challenges and prospects

    Journal of Power Sources · 2026-08-21

    Turgut M. Gür

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  21. Multimodal and cross-modal learning techniques

    APL Machine Learning · 2026-08-28

    Anand Babu, N. M. Anoop Krishnan

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  22. Machine Learning of Band Gaps and Thermodynamic Stability in A2BB′X6 (X = F, Cl, Br, I) Double Perovskites with Uncertainty-Aware Multi-Objective Screening

    Nanomaterials · 2026-09-26

    Qilin Wei, Haiyan Zhang, Chengyu Peng, Ye Tian, Yan Zhang, Yongmeng Yang, Menghong Liu, Nianxi Xu

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