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Automatic Differentiation in Quantum Chemistry with Applications to Fully Variational Hartree–Fock
ACS Central Science ( IF 18.2 ) Pub Date : 2018-05-09 00:00:00 , DOI: 10.1021/acscentsci.7b00586
Teresa Tamayo-Mendoza 1 , Christoph Kreisbeck 1 , Roland Lindh 2 , Alán Aspuru-Guzik 1, 3
Affiliation  

Automatic differentiation (AD) is a powerful tool that allows calculating derivatives of implemented algorithms with respect to all of their parameters up to machine precision, without the need to explicitly add any additional functions. Thus, AD has great potential in quantum chemistry, where gradients are omnipresent but also difficult to obtain, and researchers typically spend a considerable amount of time finding suitable analytical forms when implementing derivatives. Here, we demonstrate that AD can be used to compute gradients with respect to any parameter throughout a complete quantum chemistry method. We present DiffiQult, a Hartree–Fock implementation, entirely differentiated with the use of AD tools. DiffiQult is a software package written in plain Python with minimal deviation from standard code which illustrates the capability of AD to save human effort and time in implementations of exact gradients in quantum chemistry. We leverage the obtained gradients to optimize the parameters of one-particle basis sets in the context of the floating Gaussian framework.

中文翻译:

量子化学中的自动区分及其在完全变分哈特里克-福克中的应用

自动微分(AD)是一个功能强大的工具,它允许根据算法的所有参数计算已实现算法的导数,直至达到机器精度,而无需显式添加任何其他功能。因此,AD在量子化学中具有巨大的潜力,在这种化学中,梯度无处不在,但也很难获得,研究人员通常在实施导数时花费大量时间来寻找合适的分析形式。在这里,我们证明了在整个完整的量子化学方法中,AD可以用于计算相对于任何参数的梯度。我们介绍了DiffiQult(一种Hartree-Fock实现),它与AD工具的使用完全不同。难度是用普通Python编写的软件包,与标准代码的偏差最小,这说明了AD在实现精确的梯度化学过程中节省人工和时间的功能。我们利用获得的梯度在浮动高斯框架的背景下优化单粒子基集的参数。
更新日期:2018-05-09
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