DQC: a Python program package for Differentiable Quantum Chemistry

TL;DR

DQC is an open-source Python package that leverages automatic differentiation to simplify and accelerate various quantum chemistry calculations, enabling new applications and improving computational efficiency.

Contribution

The paper introduces DQC, a novel differentiable quantum chemistry software that integrates automatic differentiation for diverse quantum chemistry tasks.

Findings

Enables calculation of molecular perturbation properties

Allows reoptimization of basis sets for hydrocarbons

Facilitates stability checks of wave functions

Abstract

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.