English(EN) Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?

研究人员提出机器学习势能可超越训练精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

一篇新的观点论文认为，机器学习势能（MLPs）的精度可以超过用于训练它们的方法。作者提出，通过跨不同量子化学方法进行泛化，MLPs 即使不依赖实验可观测值，也能实现比其训练数据更高的精度。这种方法可能带来更精确的通用 MLPs，并重塑化学模拟训练数据集的构建和评估。 AI

影响提出了一种新的 MLP 训练方法，有望提高化学模拟的精度。

排序理由该集群包含一篇在 arXiv 上发表的研究论文，讨论了机器学习势能的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]

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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arXiv cs.AI TIER_1 English(EN) · Pavlo O. Dral, Yi-Fan Hou, Yuxinxin Chen, Hassan Nawaz, Mateusz Witkowski, Szymon \'Smiga, Ireneusz Grabowski · 2026-10-15 04:00

机器学习的潜力能否比其训练方法更准确？

arXiv:2610.18027v1 Announce Type: cross Abstract: Although machine learning potentials (MLPs) generalize across configurational and chemical compound spaces, their accuracy is commonly believed to be limited by their training methods, with improvements beyond this limit achieved …