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English(EN) Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials

新的基于Hessian的增强方法提高了MLIP的准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了两种新颖的数据增强技术UniAug和ModeAug，旨在提高机器学习原子间势（MLIP）的准确性。这些方法解决了Hessian信息（对于振动分析等任务至关重要）的整合挑战，而Hessian信息在标准的MLIP训练中常常被忽略。与需要复杂架构更改和增加计算负载的先前方法不同，UniAug和ModeAug使用简单的泰勒展开式，在不改变训练目标或扩展自动微分图的情况下有效地增强数据。这种即插即用集成可以与现有的MLIP架构无缝使用，全面的评估表明模型准确性得到了提高。 AI

影响 这些方法可以提高药物发现和材料科学分子模拟的准确性和效率。

排序理由 该集群包含一篇详细介绍机器学习原子间势新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的基于Hessian的增强方法提高了MLIP的准确性

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该集群包含一篇详细介绍机器学习原子间势新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Bumju Kwak, Jeonghee Jo ·

    基于Hessian的分子构象增强策略，用于可扩展高效的机器学习原子间势能方法

    arXiv:2609.05233v1 Announce Type: new Abstract: While machine-learning interatomic potentials (MLIPs) have successfully learned potential energy surfaces (PES) and atomic forces, many practical applications, such as vibrational analysis and transition state search, rely heavily o…

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