少年班,npj: 机器学习—快速准确猜测电子结构问题,手机凤凰网

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根据求解密度泛函理论(DFT)Kohn-Sham(KS)方程的模仿,已成为现代资料学和化学研讨和开发组合进程的重要组成部分。虽然KS方程具有很强的普适性,但因为求解核算量很大,惯例DFT核算一般只限于几百个原子。


来自佐治亚理工学院的Rampi Ramprasad领导的团队,报导了一种根据机器学习的办法,能够不直接求解KS方程而有用猜测电子结构。该办法运用新的旋转不变表明,将格点周围的原子环境映射到该格点处的电子密度和部分态密度,并运用预先核算得到的带有几百万的格点信息的DFT成果来练习的神经网络来获得该映射。上述办法能够准确模仿实践求解KS方程的成果,可是速度快几个数量级。此外,因为该办法的核算量与系统尺度严厉成线性关系,因此有望用于大型系统的电子结构猜测。

该文近期发表于npj Computational Materials 5: 22 (2019),英文标题与摘要如下,点击https://www.nature.com/articles/s41524-019-0162-7能够自在获取论文PDF。




Solving the electronic structure problem with machine learning


Anand Chandrasekaran, Deepak Kamal, Rohit Batra, Chiho Kim, Lihua Chen & Rampi Ramprasad


Simulations based on solving the Kohn-Sham (KS) equation of density functional theory (DFT) have become a vital component of modern materials and chemical sciences research and development portfolios. Despite its versatility, routine DFT calculations are usually limited to a few hundred atoms due to the computational bottleneck posed by the KS equation. Here we introduce a machine-learning-based scheme to efficiently assimilate the function of the KS equation, and by-pass it to directly, rapidly, and accurately predict the electronic structure of a material or a molecule, given just its atomic configuration. A new rotationally invariant representation is utilized to map the atomic environment around a grid-point to the electron density and local density of states at that grid-point. This mapping is learned using a neural network trained on previously generated reference DFT results at millions of grid-points. The proposed paradigm allows for the high-fidelity emulation of KS DFT, but orders of magnitude faster than the direct solution. Moreover, the machine learning prediction scheme is strictly linear-scaling with system size.



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