Kasim Muhammad F, Lehtola Susi, Vinko Sam M
Department of Physics, Clarendon Laboratory, University of Oxford, Parks Road, Oxford OX1 3PU, United Kingdom.
Molecular Sciences Software Institute, Blacksburg, Virginia 24061, USA.
J Chem Phys. 2022 Feb 28;156(8):084801. doi: 10.1063/5.0076202.
Automatic differentiation represents a paradigm shift in scientific programming, where evaluating both functions and their derivatives is required for most applications. By removing the need to explicitly derive expressions for gradients, development times can be shortened and calculations can be simplified. For these reasons, automatic differentiation has fueled the rapid growth of a variety of sophisticated machine learning techniques over the past decade, but is now also increasingly showing its value to support ab initio simulations of quantum systems and enhance computational quantum chemistry. Here, we present an open-source differentiable quantum chemistry simulation code and explore applications facilitated by automatic differentiation: (1) calculating molecular perturbation properties, (2) reoptimizing a basis set for hydrocarbons, (3) checking the stability of self-consistent field wave functions, and (4) predicting molecular properties via alchemical perturbations.
自动微分代表了科学编程中的一种范式转变，在大多数应用中，需要对函数及其导数进行求值。通过消除显式推导梯度表达式的需求，可以缩短开发时间并简化计算。由于这些原因，自动微分在过去十年中推动了各种复杂机器学习技术的快速发展，但现在它也越来越显示出其在支持量子系统的从头算模拟和增强计算量子化学方面的价值。在这里，我们展示了一个开源的可微量子化学模拟代码，并探索由自动微分促成的应用：（1）计算分子微扰性质，（2）重新优化碳氢化合物的基组，（3）检查自洽场波函数的稳定性，以及（4）通过炼金术微扰预测分子性质。
