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arXiv 2503.08400physics.chem-phquant-ph
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一种保持对称性且可迁移的表示用于学习Kohn-Sham密度矩阵

A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix

Liwei Zhang, Patrizia Mazzeo, Michele Nottoli, Edoardo Cignoni, Lorenzo Cupellini, Benjamin Stamm

AI总结:

本文提出基于ACE框架的对称性保持参数化表示,用于学习分子构型到KS密度矩阵的映射,实现系统性改进与跨分子迁移,可加速DFT计算或近似分子性质。

摘要:

Kohn-Sham(KS)密度矩阵是KS密度泛函理论(DFT)中最基本的性质之一,许多其他感兴趣的物理性质均可由它导出。本文提出了一种参数化表示,利用Atomic Cluster Expansion(ACE)框架学习从分子构型到其对应密度矩阵的映射,该表示保持了映射的物理对称性,包括等距等变性和Grassmann流形性质。在若干典型分子上训练后,所提表示被证明可随模型参数的增加而系统性改进,并且可迁移到训练集之外、甚至比训练集更复杂的分子。研究表明,由该方法生成的模型能够对密度矩阵做出合理预测,从而加速DFT计算或提供分子某些性质的近似。

英文摘要:

The Kohn-Sham (KS) density matrix is one of the most essential properties in KS density functional theory (DFT), from which many other physical properties of interest can be derived. In this work, we present a parameterized representation for learning the mapping from a molecular configuration to its corresponding density matrix using the Atomic Cluster Expansion (ACE) framework, which preserves the physical symmetries of the mapping, including isometric equivariance and Grassmannianity. Trained on several typical molecules, the proposed representation is shown to be systematically improvable with the increase of the model parameters and is transferable to molecules that are not part of and even more complex than those in the training set. The models generated by the proposed approach are illustrated as being able to generate reasonable predictions of the density matrix to either accelerate the DFT calculations or to provide approximations to some properties of the molecules.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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