DQC: a Python program package for Differentiable Quantum Chemistry

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.

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Cited by in corpus (13)

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• Automatic Differentiation for the Direct Minimization Approach to the Hartree-Fock Method
• Automatic Differentiation for Orbital-Free Density Functional Theory
• Analytical harmonic vibrational frequencies with VV10-containing density functionals: Theory, efficient implementation, and benchmark assessments
• Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation
• Inverse Hamiltonian design by automatic differentiation
• Relative energies without electronic perturbations via Alchemical Integral Transform
• Machine learning for accuracy in density functional approximations
• Performant Automatic Differentiation of Local Coupled Cluster Theories: Response Properties and Ab Initio Molecular Dynamics
• Differentiable quantum computational chemistry with PennyLane
• Basis set generation and optimization in the NISQ era with Quiqbox.jl
• Extending the definition of atomic basis sets to atoms with fractional nuclear charge
• Algorithmic differentiation for plane-wave DFT: materials design, error control and learning model parameters