English(EN) Coupled-cluster molecular properties across the main group that extrapolate beyond training size

新型AI模型以耦合簇精度预测分子性质

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-20 04:00

研究人员开发了MEHnet-MG，一种等变网络，旨在以显著降低的计算成本预测具有耦合簇精度的分子电子结构性质。该模型在一个包含九种主族元素的新数据集上进行训练，可以从一次廉价的B3LYP/def2-SVP计算中推导出能量、光学带隙和偶极矩等各种性质。与传统的密度泛函理论方法相比，MEHnet-MG的误差显著降低，并且能够准确地外插到更大的分子系统，这是基于池化的架构无法实现的。 AI

影响这项开发通过以当前计算成本的一小部分提供准确的分子性质预测，有可能显著加速计算化学研究。

排序理由该集群包含一篇详细介绍用于预测分子性质的新型AI模型的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Wenhao He, Xu Chen, Noah Song, Haowei Xu, Tim S. Hindges, Bohan Li, Zihan Lin, Yu Yao, Avetik R. Harutyunyan, Fang Liu, Yao Wang, Hao Tang, Ju Li · 2026-08-20 04:00

主族元素耦合簇分子性质外推至训练集大小之外

arXiv:2608.18346v1 Announce Type: cross Abstract: Coupled-cluster theory defines the accuracy standard for molecular electronic-structure properties but scales too steeply for routine application, whereas density-functional theory is affordable yet systematically biased. We resol…

本文如何被排名

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主族元素耦合簇分子性质外推至训练集大小之外

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