English(EN) A budget-dependent crossover between coverage- and response-based training-set selection for machine-learned interatomic potentials

新方法优化机器学习原子间势的训练数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种新的方法来选择机器学习原子间势的训练数据，这对于在原子层面模拟材料至关重要。该研究引入了一种预算依赖的交叉策略，该策略平衡了结构多样性与模型存在分歧的配置的靶向性。该方法在 MACE 模型上使用 GAP-20 Carbon 和 MD17 数据集进行了测试，证明了最优选择方法取决于保留的数据量。 AI

影响这项研究可能带来更高效、更准确的材料模拟，从而加速化学和物理等领域的发现。

排序理由该集群包含一篇学术论文，详细介绍了材料科学机器学习的新方法论。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Jia Bi, Alin-Marin Elena · 2026-09-10 04:00

面向机器学习原子间势能的基于预算的覆盖率与响应率训练集选择的交叉研究

arXiv:2609.05877v1 Announce Type: new Abstract: Selecting compact training sets for machine-learned interatomic potentials requires deciding whether to preserve structural diversity or target configurations on which models disagree. The better choice can depend on how much data i…

本文如何被排名

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面向机器学习原子间势能的基于预算的覆盖率与响应率训练集选择的交叉研究

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