发表机构
Technical University of Denmark（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该论文构建了包含1540万个第一性原理原子结构的固液界面数据集SoLiD26，涵盖多种化学元素和界面类型，用于训练和评估机器学习原子间势，并通过MACE模型验证其有效性。
AI 中文摘要
面向先进材料应用（如电化学、催化和腐蚀）中固液界面的机器学习原子间势（MLIPs），其训练数据需要同时采样液体环境、固体本体以及界面本身。我们提出了SoLiD26，一个经过整理的固液界面数据集，包含1540万个第一性原理原子结构，最多含576个原子和15种化学元素，用于训练和评估MLIPs。这些结构来源于固液界面研究中的密度泛函理论（DFT）计算，其中大部分构型来自从头算分子动力学（AIMD）模拟。每条记录包含原子种类、位置、模拟晶胞、周期性边界条件、势能和原子受力。SoLiD26包括含水贵金属界面、电极-电解质体系以及选定的体相参考结构，均使用VASP结合PBE泛函和D3色散校正计算得到。我们描述了用于构建该数据集的数据摄取和制备流程。通过在一套简单的训练、验证和测试划分上使用一系列MACE模型，展示了SoLiD26在训练和评估MLIPs方面的应用。该数据集能够支持针对结构和化学异质性固液界面的MLIPs开发与基准测试。
英文摘要
Machine-learned interatomic potentials (MLIPs) for solid-liquid interfaces in advanced materials applications, e.g., electrochemistry, catalysis and corrosion, require training data that samples both liquid environments, the solid and the interface itself. We present SoLiD26, a curated solid-liquid interface dataset, containing 15.4 million first-principles atomic structures with up to 576 atoms and 15 chemical elements for training and evaluating MLIPs. The structures were compiled from density functional theory (DFT) calculations performed in studies of solid-liquid interfaces, with most configurations originating from ab initio molecular dynamics (AIMD) simulations. Each record contains atomic species, positions, simulation cell, periodic boundary conditions, potential energy and atomic forces. SoLiD26 includes aqueous coinage metal interfaces, electrode-electrolyte systems, and selected bulk reference structures, calculated with VASP using the PBE functional and D3 dispersion corrections. We describe the data ingestion and preparation pipeline used to construct the dataset. The application of SoLiD26 for training and evaluating MLIPs is demonstrated with a suite of MACE models on a simple training, validation and test split. The dataset enables development and benchmarking of MLIPs for structurally and chemically heterogeneous solid-liquid interfaces.
