DQC: A Python program package for differentiable quantum chemistry. Kasim, M. F., Lehtola, S., & Vinko, S. M. The Journal of Chemical Physics, 156(8):084801, February, 2022. ![DQC: A Python program package for differentiable quantum chemistry [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{kasim_dqc_2022,
title = {{DQC}: {A} {Python} program package for differentiable quantum chemistry},
volume = {156},
issn = {0021-9606, 1089-7690},
shorttitle = {{DQC}},
url = {https://aip.scitation.org/doi/10.1063/5.0076202},
doi = {10.1063/5.0076202},
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 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.},
language = {en},
number = {8},
urldate = {2022-07-01},
journal = {The Journal of Chemical Physics},
author = {Kasim, Muhammad F. and Lehtola, Susi and Vinko, Sam M.},
month = feb,
year = {2022},
pages = {084801},
}
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