Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse datasets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000 times faster than DFT. While previous MLIP training datasets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) dataset provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pre-trained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation-solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion-correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations.
Computational Study of Li+ Solvation Structures in Fluorinated Ether, Non-Fluorinated Ether, and Organic Carbonate-Based Electrolytes at Low and High Salt Concentrations
Decoding the Competing Effects of Dynamic Solvation Structures on Nuclear Magnetic Resonance Chemical Shifts of Battery Electrolytes via Machine Learning
Overcoming sampling limitations using machine-learned interatomic potentials: the case of water-in-salt electrolytes
Sorbonne Université（索邦大学）；Muséum National d’Histoire Naturelle（国家自然历史博物馆）；Physicochimie des Électrolytes et Nanosystèmes Interfaciaux（电解质与界面纳米系统物理化学）；Laboratoire de Physique de L’École Normale Supérieure de Paris（巴黎高等师范学院物理实验室）
本文评估了不同MACE势在模拟21m水盐电解质中的性能，展示了 surrogate 模型如何克服 ab initio 分子动力学的采样限制，并证明了微调基础模型在数据效率和捕捉难采样构型方面的优势。
Understanding Ion Pairing in High Salt Concentration Electrolytes using Classical Molecular Dynamics Simulations and its Implications for Nonaqueous Li-O$_2$ Batteries
对溶剂化的准确理解对于合理寻找和设计能够满足锂离子电池及其他锂离子电池性能要求的电解质至关重要。在Li-O $_2$电池的情况下，离子配对在通过溶液介导的放电机制确定电池容量方面是决定性的，而不会严重损害电解质稳定性。我们认为，模型的基础上的第一溶剂化壳的counterparts的配位数是不足以描述的程度的离子配对，特别是在较高的盐浓度，往往是不符合实验观察。在这项研究中，我们使用经典的分子动力学模拟的几种盐阴离子（NO $_3^-$，BF $_4^-$，CF $_3$ SO $_3^-$，（CF $_3$ SO $_2$）$_2$ N $^-$）和非水溶剂（DMSO，DME，ACN，THF，DMA）的组合，以提高离子配对的理解的帮助下，一个新的离子配对度量。我们提出了一个度量，定义了一个阳离子的反离子和溶剂分子在一个连续的规模，如果这是基于一个简单而直观的条件下的电荷中性的限制，集群的程度。使用这些指标，我们确定的离子配对的程度与实验溶剂化相图的良好协议，并进一步讨论其有用性，在理解常用的措施，如古特曼供体数的盐和溶剂给药。
Effect of solvation shell structure on thermopower of liquid redox pairs
Recent advancements in thermogalvanic batteries offer a promising route to efficient harvesting of low-grade heat with temperatures below 100 °C. The thermogalvanic temperature coefficient α, usually referred to as effective thermopower, is the key parameter determining the power density and efficiency of thermogalvanic batteries. However, the current understanding of improving α of redox pairs remains at the phenomenological level without microscopic insights, and the development of electrolytes with high α largely relies on experimental trial and error. This work applies the free energy pert…
The Association of Zn$^{2+}$-SO$_4^{2-}$ and Mg$^{2+}$-SO$_4^{2-}$ in Aqueous MgSO$_4$/ZnSO$_4$ Hybrid Electrolytes: Insights from All-Atom Molecular Dynamics Simulations Molecular Dynamics Simulations
Calculated state-of-the art results for solvation and ionization energies of thousands of organic molecules relevant to battery design
Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Vienna（维也纳大学）；Uppsala University（乌普萨拉大学）；Vector Institute for Artificial Intelligence（人工智能向量研究所）；University of Toronto（多伦多大学）；Technische Universität Berlin（柏林工业大学）
Extracting a nitrile-centered, ether-assisted motif hierarchy for lithium-battery electrolyte design from billion-scale molecular space
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Suzhou Big Data & AI Research and Engineering Center（苏州大数据与人工智能研究与工程中心）；Contemporary Amperex Technology Co. Limited（宁德时代新能源科技股份有限公司）；AI for Science Institute（人工智能科学研究所）
Are Weakly Coordinating Anions Really the Holy Grail of Ternary Solid Polymer Electrolytes Plasticized by Ionic Liquids? Coordinating Anions to the Rescue of the Lithium Ion Mobility
Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces
Leiden Institute of Chemistry, Leiden University（莱顿大学化学研究所）；State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University（厦门大学化学化工学院固体表面物理化学国家重点实验室）；Department of Chemical Engineering, University of Michigan（密歇根大学化学工程系）；School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University（南洋理工大学化学、化学工程与生物技术学院）
Differentiable OPLS Force Field Parameterization for Ionic Electrolytes and High-Throughput Application to Lithium-ion Batteries
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；CATL 21C Innovation Laboratory, Contemporary Amperex Technology Ltd.（宁德时代21世纪创新实验室）；Department of Physics, Institute of Quantum Science and Technology, Shanghai University（上海大学量子科学与技术研究院物理系）；Shenzhen All-Solid-State Lithium Battery Electrolyte Engineering Research Center, Institute of Materials Research (IMR), Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院材料研究院深圳全固态锂电池电解质工程研究中心）
Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
由于计算成本高，使用{ \it ab initio}分子动力学（AIMD）在室温下精确模拟锂离子在电池材料中的扩散过程一直是一个挑战。近年来，由于基于机器学习的原子间势的进步，这种情况发生了巨大变化。在这里，我们实现了深电位发生器计划，以\textit{automatically}产生LiGePS型固态电解质材料的原子间相互作用势。这增加了我们模拟这些材料的能力，在不牺牲{ \it ab initio}精度的情况下，提高了几个数量级。重要的技术方面，如统计误差和尺寸效应进行了仔细研究。我们进一步建立了一个可靠的协议，在实验条件下准确计算锂离子扩散过程，通过调查重要的技术方面，如统计误差和尺寸效应。这样的协议和自动化工作流程使我们能够以更高的效率筛选材料的相关属性。通过使用这里开发的协议和自动化的工作流程，我们得到的扩散系数数据和锂离子扩散的活化能，以及同意与实验。本文的工作为进一步研究锂离子扩散机理和优化固态电解质材料的锂离子导电性能奠定了基础。
Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion-Pairing
Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
Department of Material Science and Engineering, Massachusetts Institute of Technology（材料科学与工程系，麻省理工学院）；Institute of Theoretical Chemistry, University of Vienna（理论化学研究所，维也纳大学）；Department of Physical and Macromolecular Chemistry, Charles University（物理与大分子化学系，查尔斯大学）
