New ontology standardizes machine learning interatomic potentials

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-28 04:00

研究人员开发了一种新的本体，即 MLIPs 本体，用于标准化机器学习原子间势（MLIPs）的描述。这个 OWL 2 DL 本体旨在通过捕获与 MLIP 方法、其超参数、具有 DFT 出处的训练数据集以及基准测试相关的概念来解决该领域分散的元数据问题。该本体结构分为三个模块：方法、训练数据和基准测试，并与材料科学和机器学习中的现有本体集成。 AI

影响标准化 MLIPs 的元数据，有望提高材料科学研究的可复现性和协作性。

排序理由该集群描述了一篇介绍机器学习特定子领域本体的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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New ontology standardizes machine learning interatomic potentials

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Daniel Hern\'andez, Jong Hyun Jung, Yuji Ikeda, Yongliang Ou, Pranav Kumar, Tom Sch\"achtel, Wenchuan Liu, Xin Li, Xi Zhang, Xiang Xu, Lifang Zhu, Fritz K\"ormann, Steffen Staab, Blazej Grabowski · 2026-07-28 04:00

机器学习原子间势的本体论

arXiv:2607.23219v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) approximate quantum-mechanical energies and forces---conventionally computed by density functional theory (DFT) or wave-function methods---at a fraction of the cost. The field encompas…

本文如何被排名

报道来源 [1]

机器学习原子间势的本体论

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