English(EN) Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation

基于MLIP的新方法增强材料生成和评估

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-03 04:00

研究人员引入了一种新方法，利用预训练机器学习原子间势（MLIPs），特别是MACE的表征来生成和评估无机晶体结构。他们开发了一种粗粒度-精细度传输距离（CFTD）指标来评估生成材料的质量和新颖性，并将其与现有的SUN指标进行了比较。研究表明，这些粗粒度的MACE特征可以有效地指导材料发现的生成模型。 AI

影响这项研究通过提高生成模型的效率和准确性，有可能加速新材料的发现。

排序理由该集群包含一篇学术论文，详细介绍了一种用于材料生成和评估的新方法和指标。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Paul Hagemann, Katharina Ueltzen, Simon M\"uller, Janine George, Philipp Benner · 2026-08-03 04:00

预训练机器学习原子间势能的表征作为材料生成和评估的粗粒度坐标

arXiv:2607.28776v1 Announce Type: new Abstract: Generative machine learning is increasingly used for inorganic crystal structure generation. Most models and the corresponding evaluation approaches rely on simple forms of crystal structure representation. In this paper, we showcas…

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报道来源 [1]

预训练机器学习原子间势能的表征作为材料生成和评估的粗粒度坐标

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