Tamayo-Mendoza Teresa, Kreisbeck Christoph, Lindh Roland, Aspuru-Guzik Alán
Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, United States.
Department of Chemistry-Ångström, The Theoretical Chemistry Programme, Uppsala Center for Computational Chemistry, UC3, Uppsala University, Box 518, 751 20, Uppsala, Sweden.
ACS Cent Sci. 2018 May 23;4(5):559-566. doi: 10.1021/acscentsci.7b00586. Epub 2018 May 9.
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 , a Hartree-Fock implementation, entirely differentiated with the use of AD tools. 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.
自动微分（AD）是一种强大的工具，它能够以机器精度计算已实现算法相对于其所有参数的导数，而无需显式添加任何额外函数。因此，AD在量子化学中具有巨大潜力，在量子化学中梯度无处不在但又难以获得，并且研究人员在实现导数时通常要花费大量时间寻找合适的解析形式。在这里，我们证明了AD可用于在完整的量子化学方法中计算相对于任何参数的梯度。我们展示了一个完全使用AD工具进行微分的Hartree-Fock实现。是一个用纯Python编写的软件包，与标准代码的偏差最小，它展示了AD在量子化学精确梯度实现中节省人力和时间的能力。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组的参数。
