The paper introduces EMolStudio, a density‑matrix‑centered AI platform that predicts and analyzes electronic structure for electrolyte molecules in lithium batteries. It integrates molecular functionalization, explicit Li⁺ first‑shell assembly, density‑matrix prediction, and electronic‑structure parsing, enabling systematic study of 163,655 functionalized molecules and 22,500 first‑shell clusters across four lithium salts. The analysis reveals that functional groups and anion identity distinctly influence frontier orbital energies, electrostatic potential, and Li⁺‑donor contacts, with LiTDI anchoring the highest occupied orbital on the anion.
By Mingkang Liu, Huize Yu, Lei Shen
The paper presents a methodological framework that links the FINALES experiment orchestration system with the Kadi4Mat research data management ecosystem to create interoperable, automated workflows for battery research. By coordinating experiment planning, execution, data handling, and analysis across distributed sites, the framework enables reproducible, end‑to‑end experimental pipelines. The authors demonstrate its utility by studying sodium‑ion coin cell formation, using a Gaussian process model to map formation parameters to electrochemical performance and identify promising experimental regions.
By Giovanna Tosato (Karlsruhe Institute of Technology), Leon Merker (Karlsruhe Institute of Technology, Helmholtz Institute Ulm, Technical University of Munich), Monika Vogler (Technical University of Munich), Michael Selzer (Karlsruhe Institute of Technology), Arnd Koeppe (Karlsruhe Institute of Technology)
The increasing share of renewable energy in power systems creates a need for fast-response and flexible resources to maintain system stability. With the expansion of electricity markets and ancillary...
arXiv:2608. 16238v1 Announce Type: new Abstract: The increasing share of renewable energy in power systems creates a need for fast-response and flexible resources to maintain system stability.
By Chunyang Zhao, Stoyan Trenchev, Shi You, Chresten Tr{\ae}holt
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:2607. 25597v1 Announce Type: new Abstract: The reactivity of lithium-metal electrolytes arises from the interplay of molecular functional groups, Li$^+$ solvation, and salt-anion participation.
By Mingkang Liu, Huize Yu, Yanbin Gao, Nan Yao, Xiang Chen, Lei Shen
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
The study uses large‑scale, quantum‑accurate reactive simulations powered by active learning and deep equivariant neural network potentials to investigate the coupled reactions and diffusion at the interface of a symmetric solid‑state battery cell. Unsupervised clustering of local atomic environments reveals a previously unreported crystalline disordered phase, Li₂S₀.₇₂P₀.₁₄Cl₀.₁₄, in the solid‑electrolyte interphase (SEI), and explains experimental observations of SEI formation and lithium creep mechanisms that drive dendrite initiation. The work demonstrates a parameter‑free digital twin capable of providing atomistic insights into complex heterogeneous processes in solid‑state electrochemistry.
By Jingxuan Ding, Laura Zichi, Matteo Carli, Menghang Wang, Albert Musaelian, Yu Xie, Boris Kozinsky
arXiv:2606. 02618v1 Announce Type: cross Abstract: We present Cognitive Loop via In-Situ Optimization (CLIO), an agent that couples a continuously-updated belief-state graph with a recursive plan-then-act loop.
By Newman Cheng, Gordon Broadbent IV, Jason Dong, Syed Mohammed Ali Hussaini, Farman Ullah, Morris Sharp, Gabrielle Barnes, Nanlin Guo, Deyu Zou, Karin Strauss, William Chappell, David G. Kwabi, Bichlien H. Nguyen, Jake A. Smith
arXiv:2606. 28220v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models.
By Gift Modekwe, Qiugang Lu
arXiv:2607. 19114v1 Announce Type: new Abstract: Activities in aqueous electrolyte solutions, usually described by ionic activity and osmotic coefficients, are important properties for modeling many processes in industry and nature.
By Zeno Romero, Maximilian Kohns, Fabian Jirasek
arXiv:2607. 29095v1 Announce Type: new Abstract: Accurate prediction of lithium-ion battery state of health (SOH) is essential for reliable energy storage operation.
By Zeping Chen, Ruda Jian, Sachin Sigdel, Guoping Xiong, Jian-Xun Wang, Tengfei Luo