Machine learning force field molecular dynamics simulation of interfacial reactions on platinum in concentrated aqueous electrolytes

IF 13.1 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Hao Yu, Norio Takenaka, Atsuo Yamada
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引用次数: 0

Abstract

Aqueous lithium-ion batteries provide a promising route toward safe and sustainable energy storage, yet their energy density is constrained by the narrow electrochemical stability window of aqueous electrolytes. Although high salt concentrations can kinetically extend the cathodic limit of the electrolyte via anion-derived interphases, this effect depends strongly on the electrode material and is absent on platinum. To elucidate the interfacial reaction mechanisms behind the absence of cathodic-limit extension on platinum, we performed molecular dynamics simulations based on a machine learning force field, combining near first-principles accuracy with large-scale configurational sampling. The simulations reveal a dominant pathway for anion decomposition on a hydrogen-covered platinum surface under cathodic conditions, mediated by adsorbed hydrogen (H*). However, this pathway directly competes with hydrogen evolution via the Tafel step (2H* → H2) at comparable activation barriers, suppressing the formation of stable anion-derived interphases and the extension of the cathodic limit. These findings reveal interfacial H* coverage as a key factor governing electrode-dependent cathodic stabilization in concentrated aqueous electrolytes.
浓缩水溶液中铂界面反应的机器学习力场分子动力学模拟
含水锂离子电池为安全和可持续的能量存储提供了一条有前途的途径,但其能量密度受到含水电解质狭窄的电化学稳定窗口的限制。虽然高盐浓度可以通过阴离子衍生的界面相动态地延长电解质的阴极极限,但这种影响强烈依赖于电极材料,而在铂上则不存在。为了阐明铂上阴极极限延伸缺失背后的界面反应机制,我们基于机器学习力场进行了分子动力学模拟,将接近第一性原理的精度与大规模构型采样相结合。模拟结果揭示了负离子在阴极条件下由吸附氢(H*)介导的氢覆盖铂表面分解的主要途径。然而,这一途径直接与通过Tafel步骤(2H*→H2)在类似的激活障碍下析氢竞争,抑制了稳定阴离子衍生的界面相的形成和阴极极限的扩展。这些发现表明,界面H*覆盖是控制电极依赖的阴极稳定性的关键因素。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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