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New dataset SoLiD26 advances MLIPs for solid-liquid interface simulations

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SoLiD26, a new dataset designed to train machine-learned interatomic potentials (MLIPs) for simulating solid-liquid interfaces. This dataset comprises 15.4 million first-principles structures, including up to 576 atoms and 15 chemical elements, derived from density functional theory calculations and ab initio molecular dynamics simulations. SoLiD26 aims to address the limitations of existing datasets by providing comprehensive sampling of these interfaces, which are crucial for understanding energy and catalytic materials. The dataset has been demonstrated to be effective in training MACE models for applications in electrochemical and catalytic systems. AI

IMPACT Enables more accurate and scalable simulations for materials science, potentially accelerating discovery in energy and catalysis.

RANK_REASON The cluster describes a new dataset and associated research paper for training machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset SoLiD26 advances MLIPs for solid-liquid interface simulations

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The cluster describes a new dataset and associated research paper for training machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jonas Busk, Emil J. P. Frost, Yogeshwaran Krishnan, Henrik H. Kristoffersen, August E. G. Mikkelsen, Xueping Qin, Xin Yang, Heine A. Hansen, Arghya Bhowmik, Tejs Vegge ·

    SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials

    arXiv:2609.28013v2 Announce Type: replace-cross Abstract: Machine-learned interatomic potentials (MLIPs) enable simulations of larger atomistic systems over longer timescales than traditional ab initio methods but require large, diverse training data. Existing datasets lack compr…

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