SoLiD26 数据集发布：含 1540 万原子结构，用于训练固液界面机器学习势
SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials
搞材料计算的朋友可以看看，1540 万个固液界面 DFT 结构直接可用，配 MACE 模型就能训练自己的机器学习势。
SoLiD26 是一个面向固液界面的第一性原理数据集，包含 1540 万个 DFT 计算的原子结构，单结构最多 576 个原子，覆盖 15 种化学元素。数据主要来自从头算分子动力学（AIMD）模拟，用 VASP 的 PBE 泛函加 D3 色散修正计算，涵盖水系币族金属界面和电极-电解质体系。论文用一套 MACE 模型演示了基于该数据集的 MLIP 训练与测试划分流程。数据集面向电化学、催化和腐蚀等应用场景的势函数开发与基准评测。
SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials
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.