DQC: a Python program package for Differentiable Quantum Chemistry
This work provides a tool for computational chemists to accelerate and enhance quantum simulations, though it is incremental as it applies existing automatic differentiation techniques to quantum chemistry.
The authors tackled the challenge of simplifying gradient calculations in quantum chemistry by developing DQC, an open-source differentiable quantum chemistry simulation code, which enables applications like calculating molecular perturbation properties and reoptimizing basis sets for hydrocarbons.
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 be shortened, and calculations 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 {\it ab initio} simulations of quantum systems, and enhance computational quantum chemistry. Here we present an open-source differentiable quantum chemistry simulation code, DQC, 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.