ElectrolyteFM is a unified multi‑property prediction model designed to improve electrolyte formulation design by learning property‑specific representations and cross‑property knowledge through an expert pool and a routing mechanism. The model selectively incorporates shared information for each target property, using residual adapters to refine predictions. Experiments on the Electrolyte12 dataset show a 14.8% reduction in normalized mean absolute error across 12 properties, and on an unseen sodium‑electrolyte dataset it achieves a 6.7% lower conductivity mean absolute error compared to the best baseline.
By Jiaxin Yu, Shuo Wang, Peng Wang, Yongcai Wang, Deying Li
The article examines how scientific literature, often used as a data source for AI in materials science, can contain hidden inaccuracies such as text-figure mismatches, ambiguous axis labels, unit inconsistencies, and missing measurement context. By tracing solid electrolyte conductivity values from original papers to curated datasets, the authors uncover recurrent errors that are numerically plausible yet hard to detect, leading to significant label noise in AI models. A cross-database example demonstrates that ambiguous reporting can cause a 100‑fold error in conductivity values, underscoring the need for traceable reporting, rigorous curation, and validation practices in AI-driven discovery.
By Qian Wang, Ying Li, Ryuhei Sato, Hidemi Kato, Shin-ichi Orimo, Hao Li, Eric Jianfeng Cheng
arXiv:2604. 22938v2 Announce Type: replace-cross Abstract: The promise of data-driven materials discovery remains constrained by the scarcity of large, high-quality, and accessible experimental datasets.
By Zhanzhao Li, Kengran Yang, Qiyao He, Kai Gong
The review traces the development of machine‑learning techniques for extracting materials data and knowledge from scientific literature, progressing from rule‑based pipelines to NLP models, transformer encoders, and finally large language models and agentic systems that handle text, tables, and figures. It highlights how these methods enable the creation of queryable materials databases, knowledge graphs, generic question answering, AI co‑scientists, chatbots, and closed‑loop discovery with autonomous labs. The paper critically evaluates datasets, extraction accuracy, computational costs, and offers practical guidelines, while outlining open challenges such as full tetrahedron extraction, benchmark scarcity, and terminology resolution.
By Aravindan Kamatchi Sundaram, B. Pabitramohan Prusty, Rohit Batra
RxnExBench is a multimodal benchmark designed to evaluate vision‑language models on the task of extracting structured chemical reaction data from dense figures in synthetic chemistry literature. The dataset, compiled from high‑impact journals, contains 3,565 expert‑annotated reactions and 2,361 molecular structures, organized under a unified schema of 15 reaction roles and 26 core fields. It also introduces a difficulty taxonomy, a modular annotation pipeline, and the Oracle‑MolRec protocol to separate molecular recognition from higher‑level reasoning, and shows that current state‑of‑the‑art models struggle as schema complexity and reasoning demands increase.
By Jiaxing Sun, Junyuan Gao, Ruikang Zhang, Hao Zheng, Lingli Ge, Xiao An, Jiang Wu, Huazheng Zeng, Jiahe Song, Chen Zhu, Haote Yang, Jingchao Wang, Lu Chen, Yuanyuan Cao, Ruijie Zhang, Shengxin Lu, Weijia Li, Lijun Wu, Conghui He
arXiv:2607. 09806v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming scientific discovery, but its effectiveness is fundamentally limited by the availability of structured scientific knowledge.
By Dibakar Datta
arXiv:2607. 09762v1 Announce Type: new Abstract: Public battery aging datasets are a critical asset for advanced health management, but their practical use is often limited by inconsistent formats, unclear schemas, and metadata scattered across repositories and publications.
By Tianwen Zhu, Hao Wang, Yonggang Wen
ProtoMI is a literature‑driven framework that learns structural priors from 126 reported boron‑containing electrolyte additives and applies them to screen 179,977 unlabeled candidates. Using graph contrastive learning, it identifies seven interpretable prototypes and adapts them through semi‑supervised contrastive learning, achieving enrichment factors of 9.2–45.6 while evaluating less than 2% of the candidate space. The method led to the discovery of four commercially accessible additives, including TNDB, which improves high‑temperature LiFePO4||graphite cycling by 34.93% and forms protective interphases that suppress solvent decomposition and Fe deposition.
By Weixiang Hong, Hongting Du, Jiayue Tang, Ruifeng Tan, Yangjian Quan, Jia Li, Jiaqiang Huang
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
By Jacob W. Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes K\"astner, Heather J. Kulik
arXiv:2607. 23886v1 Announce Type: cross Abstract: X-ray absorption spectroscopy (XAS) is central to understanding the local electronic and atomic structure of materials, yet most published spectra remain inaccessible to data-driven analysis because they are embedded in figures and described through fragmented textual context in the literature.
By Tanjin He, Aikaterini Vriza, Logan Ward, Xu Huang, Yiming Chen, Anubhav Jain, Gerbrand Ceder, Rajeev S. Assary, Ian T. Foster, Maria K. Y. Chan
arXiv:2602. 02320v4 Announce Type: replace-cross Abstract: Molecular function is largely determined by structure.
By Feiyang Cai, Guijuan He, Yi Hu, Jingjing Wang, Joshua Luo, Tianyu Zhu, Srikanth Pilla, Gang Li, Ling Liu, Feng Luo
The article reviews how Large Models (LMs) based on Transformer architectures and self‑supervised pre‑training can address longstanding challenges in Battery Prognostics and Health Management (BPHM). It surveys LM applications across data scarcity, generalization, interpretability, and system automation, and outlines a roadmap for future research, including collaborative data ecosystems, validation, trustworthiness, and efficient deployment. The review aims to guide researchers and practitioners in developing next‑generation battery management systems that are safe, reliable, and autonomous throughout battery lifecycles.
By Jiale Liu, Huan Wang, Weicheng Wang, Rong Zhu, Qiqi Wang, Min Xie