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arXiv 2607.04095physics.chem-phcond-mat.mtrl-scinucl-thquant-ph
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用于密度泛函理论的机器学习与人工智能:Kohn-Sham 与无轨道密度泛函理论、电子与核密度泛函理论的不同优先级

ML and AI for density functional theory: different priorities for Kohn-Sham and orbital-free DFT, for electronic and nuclear DFT

Fuzhou University(福州大学);Institute of Science Tokyo(东京科学大学)

Xin-Hui Wu, Sergei Manzhos

AI总结:

概述机器学习和人工智能技术在电子与核密度泛函理论中计算瓶颈及精度要求的异同,指出不同情况下的方法选择,如 KS DFT 中神经网络的优缺点,及符号回归的优势,还提及电子到核 DFT 的方法。

摘要:

我们概述了在电子和核密度泛函理论中,可通过机器学习(ML)和人工智能(AI)技术解决的计算瓶颈和精度要求方面的异同。由此得出,根据是机器学习交换关联(XC)泛函、动能泛函(KEF)、密度还是基函数,会有不同的有前景的方法和算法选择。特别是,虽然流行的深度神经网络在 KS DFT 中仍然是一个有力选择,但我们强调了它们在构建 KEF 时的缺点,并强调了符号回归在电子和核 DFT 中的概念优势(尚未完全实现)。我们指出了可以从研究更广泛的 ML 增强电子 DFT 应用到核 DFT 的有前景的方法。

英文摘要:

We overview similarities and, importantly, differences in computational bottlenecks and accuracy requirements that can be addressed with machine learning (ML) and artificial intelligence (AI) techniques in electronic and nuclear DFT. From these follow different promising methodological and algorithmic choices depending on whether one machine learns the exchange correlation (XC) functional, the kinetic energy functional (KEF), the density or the basis functions. In particular, while the popular deep neural networks remain a potent choice in the context of KS DFT, we highlight their disadvantages when building KEFs and highlight conceptual advantages - yet to be fully realized - of symbolic regression for both electronic and nuclear DFT. We point out promising approaches that can be carried from the more extensively investigated ML-enhanced electronic DFT to nuclear DFT.

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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