Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies

Abstract A major goal of materials research is the discovery of novel and efficient heterogeneous catalysts for various chemical processes. In such studies, the candidate catalyst material is modeled using tens to thousands of chemical species and elementary reactions. Density Functional Theory (DFT...

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Autores principales:	Himaghna Bhattacharjee, Nikolaos Anesiadis, Dionisios G. Vlachos
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aacb8c675ade43e7b47d6c552499e479
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spelling	oai:doaj.org-article:aacb8c675ade43e7b47d6c552499e4792021-12-02T16:14:09ZRegularized machine learning on molecular graph model explains systematic error in DFT enthalpies10.1038/s41598-021-93854-w2045-2322https://doaj.org/article/aacb8c675ade43e7b47d6c552499e4792021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93854-whttps://doaj.org/toc/2045-2322Abstract A major goal of materials research is the discovery of novel and efficient heterogeneous catalysts for various chemical processes. In such studies, the candidate catalyst material is modeled using tens to thousands of chemical species and elementary reactions. Density Functional Theory (DFT) is widely used to calculate the thermochemistry of these species which might be surface species or gas-phase molecules. The use of an approximate exchange correlation functional in the DFT framework introduces an important source of error in such models. This is especially true in the calculation of gas phase molecules whose thermochemistry is calculated using the same planewave basis set as the rest of the surface mechanism. Unfortunately, the nature and magnitude of these errors is unknown for most practical molecules. Here, we investigate the error in the enthalpy of formation for 1676 gaseous species using two different DFT levels of theory and the ‘ground truth values’ obtained from the NIST database. We featurize molecules using graph theory. We use a regularized algorithm to discover a sparse model of the error and identify important molecular fragments that drive this error. The model is robust to rigorous statistical tests and is used to correct DFT thermochemistry, achieving more than an order of magnitude improvement.Himaghna BhattacharjeeNikolaos AnesiadisDionisios G. VlachosNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Himaghna Bhattacharjee Nikolaos Anesiadis Dionisios G. Vlachos Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies
description	Abstract A major goal of materials research is the discovery of novel and efficient heterogeneous catalysts for various chemical processes. In such studies, the candidate catalyst material is modeled using tens to thousands of chemical species and elementary reactions. Density Functional Theory (DFT) is widely used to calculate the thermochemistry of these species which might be surface species or gas-phase molecules. The use of an approximate exchange correlation functional in the DFT framework introduces an important source of error in such models. This is especially true in the calculation of gas phase molecules whose thermochemistry is calculated using the same planewave basis set as the rest of the surface mechanism. Unfortunately, the nature and magnitude of these errors is unknown for most practical molecules. Here, we investigate the error in the enthalpy of formation for 1676 gaseous species using two different DFT levels of theory and the ‘ground truth values’ obtained from the NIST database. We featurize molecules using graph theory. We use a regularized algorithm to discover a sparse model of the error and identify important molecular fragments that drive this error. The model is robust to rigorous statistical tests and is used to correct DFT thermochemistry, achieving more than an order of magnitude improvement.
format	article
author	Himaghna Bhattacharjee Nikolaos Anesiadis Dionisios G. Vlachos
author_facet	Himaghna Bhattacharjee Nikolaos Anesiadis Dionisios G. Vlachos
author_sort	Himaghna Bhattacharjee
title	Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies
title_short	Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies
title_full	Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies
title_fullStr	Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies
title_full_unstemmed	Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies
title_sort	regularized machine learning on molecular graph model explains systematic error in dft enthalpies
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aacb8c675ade43e7b47d6c552499e479
work_keys_str_mv	AT himaghnabhattacharjee regularizedmachinelearningonmoleculargraphmodelexplainssystematicerrorindftenthalpies AT nikolaosanesiadis regularizedmachinelearningonmoleculargraphmodelexplainssystematicerrorindftenthalpies AT dionisiosgvlachos regularizedmachinelearningonmoleculargraphmodelexplainssystematicerrorindftenthalpies
_version_	1718384369324785664

Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies

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