新基准揭示机器学习原子间势能模型在处理未见分子组成时存在困难

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

一个新基准已被开发出来，用于评估机器学习原子间势能模型（MLIPs）的组合泛化能力。这些势能模型对于包括药物设计和材料发现等各种科学应用至关重要。该基准显示，即使是基于数百万分子训练的先进模型，当前最先进的MLIPs在处理未见的分子组成时也面临显著困难，其误差比在分布内数据上的误差高出一个数量级。 AI

影响强调了当前MLIPs的局限性，表明需要更深入理解化学组成的模型来促进科学发现。

排序理由该集群包含一篇学术论文，详细介绍了一个用于评估机器学习模型的新基准。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Amir Masoud Nourollah, Irtaza Khalid, Stefano Leoni, Steven Schockaert · 2026-10-23 04:00

Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials

arXiv:2605.08988v2 Announce Type: replace Abstract: Machine Learning Interatomic Potentials play a fundamental role in computational chemistry and materials science, enabling applications from molecular dynamics simulations to drug design and materials discovery. While recent app…

本文如何被排名

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Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials

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