A systematic evaluation and multiscale extension of the framework proposed by Yang et al. is presented, showing how data size and composition affect robustness, and demonstrating improved cross-system transferability and multiscale performance over baselines.
Rational design of high‐performance electrolytes is central to the development of next‐generation lithium batteries, but it remains hindered by vast molecular design space, intricate multiscale structure–property relationships, and multiple performance requirements. Although machine learning (ML) accelerates electrol...
Hao-Hao Chen, Hong Xu, Qian Zhou et al.· Advanced Energy Materials· 0 citations
This review addresses a critical need in the emerging intersection of digital chemistry and material properties by critically evaluating the current capabilities and future prospects of applying machine learning (ML) to ionic liquids (ILs) for thermal energy storage (TES) applications. We examine available data sets...
Pawan Chhipa, Z. Baird, Dinis O. Abranches et al.· Journal of Chemical Informat...· 0 citations
A hierarchical synergistic deep-learning framework that sequentially coordinates efficiency, accuracy, and reliability through four complementary modules that outperforms mainstream counterparts in its task, while retrospective validation establishes dual closed-loop verification of module-level accuracy and end-to-end...
Hong-Wei Du, Ding-Yang Lv, Bao-Le Wei et al.· 0 citations
This work aims to establish a foundational roadmap for the data‐driven and rational design of advanced HSSEs, thereby advancing the realization of next‐generation ASSLBs.
Jia-Hui Ye, Ming Gao, Min-Yu Jia et al.· Advanced Functional Material...· 0 citations
The rise of data science and decades of accumulated battery research have paved the way for the general use of computing in research. Here, the largest experimental coulombic efficiency (CE) dataset curated from over 100 scientific articles under strict selection criteria was analyzed to extract insights that would hav...
Minh Van Duong, Mitchell E. Kaiser, Xia Cao et al.· Advances in Materials· 0 citations
This study provides a structured and reproducible assessment of the conditions under which descriptor-based ML models can be expected to succeed or fail in DES systems and highlights the importance of rigorous, leakage-aware evaluation in data-driven chemical modeling.
Hakim Faraji, Julio Brito Santana, R. Rodríguez-Ramos et al.· ACS Omega· 0 citations
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