English(EN) M\=oLe-{\Lambda}: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties

MōLe-Λ：AI模型加速量子化学计算

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-29 04:00

研究人员开发了MōLe-Λ，一种新颖的机器学习模型，旨在更有效地预测量子化学性质。该模型扩展了现有的MōLe框架，以学习耦合簇（CC）响应态的右侧（T）和左侧（Λ）幅度。通过从局部分子轨道联合学习这些幅度，MōLe-Λ可以准确预测能量、力、偶极矩、四极矩、极化率和电子密度，与传统的CCSD计算相比具有显著的速度优势。 AI

排序理由该集群包含一篇详细介绍用于量子化学计算的新机器学习模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍用于量子化学计算的新机器学习模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Andreas Burger, Luca Thiede, Abdulrahman Aldossary, Jorge A. Campos-Gonzalez-Angulo, Alex Zook, J\'er\^ome Florian Gonthier, Al\'an Aspuru-Guzik · 2026-05-29 04:00

M\=oLe-{\Lambda}：学习耦合簇响应态以获得能量、梯度和性质

arXiv:2605.29622v1 Announce Type: new Abstract: Coupled-cluster (CC) theory is often considered the gold standard of quantum chemistry, but its high computational cost limits routine access to accurate energies, forces and response properties. While the right-hand $T$-amplitudes …

本文如何被排名

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M\=oLe-{\Lambda}：学习耦合簇响应态以获得能量、梯度和性质

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