Npj Comput. Mater.:材料预测—精确可传递
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来自英国华威大学工程学院华威预测建模中心的James R. Kermode教授领导的研究团队,提出了一种完全数据驱动的方法,以第一性原理计算数据为基础构建了哈密顿量预测模型。为实现该方法,作者引入了原子团簇扩展(ACE)描述符来表征哈密顿量和交叠矩阵中原子内的在位区块和原子间的非在位区块,这些描述符以及对应的哈密顿量和交叠矩阵在三维空间中对完全旋转群具有等价变换的特性。该方法给出了哈密顿量和交叠矩阵的线性模型,在非正交局域原子轨道表象下,可以如实地反映作为原子构型及材料构成的函数的电子结构。此外,所构建的模型超出了传统的紧束缚近似描述,因为它全阶地表征了第一性原理模型,而非采用双中心或三中心近似。作者通过预测不同晶体系统中块状铝的能带结构,展示了该方法的能力。本文所提出的方法在开发精度可与DFT相比,且可适用的时间与空间尺度超过DFT的机器学习框架,在新材料的预测方面具有巨大的潜在价值。
该文近期发表于npj Computational Materials 8:158(2022),英文标题与摘要如下,点击左下角“阅读原文”可以自由获取论文PDF。
Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
Liwei Zhang, Berk Onat, Geneviève Dusson, Adam McSloy, G. Anand, Reinhard J. Maurer, Christoph Ortner & James R. Kermode
We propose a scheme to construct predictive models for Hamiltonian matrices in atomic orbital representation from ab initio data as a function of atomic and bond environments. The scheme goes beyond conventional tight binding descriptions as it represents the ab initio model to full order, rather than in two-centre or three-centre approximations. We achieve this by introducing an extension to the atomic cluster expansion (ACE) descriptor that represents Hamiltonian matrix blocks that transform equivariantly with respect to the full rotation group. The approach produces analytical linear models for the Hamiltonian and overlap matrices. Through an application to aluminium, we demonstrate that it is possible to train models from a handful of structures computed with density functional theory, and apply them to produce accurate predictions for the electronic structure. The model generalises well and is able to predict defects accurately from only bulk training data.
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