AdaBoost Ensemble Correction Models for TDDFT Calculated Absorption Energies

AdaBoost Ensemble Correction Models for TDDFT Calculated Absorption Energies

Molecular excited states are important for molecular optical properties, which can be feasibly explored by quantum chemical calculations. However, the computation is highly demanding due to their complicated characteristic features. Therefore, high accuracy and unambiguous descriptions are strongly...

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Main Authors: Jingxia Cui, Wenze Li, Chao Fang, Shunting Su, Jiaoyang Luan, Ting Gao, Lihong Hu, Yinghua Lu, Guanhua Chen
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Ensemble learning
AdaBoost
regression
TDDFT
absorption energies
Online Access:https://ieeexplore.ieee.org/document/8668753/
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spelling doaj-68c3eb3bd4704913bd4fe7e17c95413e2021-04-05T17:01:05ZengIEEEIEEE Access2169-35362019-01-017383973840610.1109/ACCESS.2019.29059288668753AdaBoost Ensemble Correction Models for TDDFT Calculated Absorption EnergiesJingxia Cui0Wenze Li1Chao Fang2Shunting Su3Jiaoyang Luan4Ting Gao5Lihong Hu6https://orcid.org/0000-0003-3792-2917Yinghua Lu7Guanhua Chen8Faculty of Chemistry, Institute of Functional Material Chemistry, Northeast Normal University, Changchun, ChinaSchool of Information Science and Technology, Northeast Normal University, Changchun, ChinaSchool of Information Science and Technology, Northeast Normal University, Changchun, ChinaSchool of Information Science and Technology, Northeast Normal University, Changchun, ChinaSchool of Information Science and Technology, Northeast Normal University, Changchun, ChinaSchool of Information Science and Technology, Northeast Normal University, Changchun, ChinaSchool of Information Science and Technology, Northeast Normal University, Changchun, ChinaFaculty of Chemistry, Institute of Functional Material Chemistry, Northeast Normal University, Changchun, ChinaDepartment of Chemistry, The University of Hong Kong, Hong KongMolecular excited states are important for molecular optical properties, which can be feasibly explored by quantum chemical calculations. However, the computation is highly demanding due to their complicated characteristic features. Therefore, high accuracy and unambiguous descriptions are strongly desired for excited state investigations. This paper proposes accurate, robust, and efficient ensemble correction models for absorption calculations with the most used quantum chemical method, time-dependent density functional theory (TDDFT). Models are built by AdaBoost framework with both weak machine learning: support vector machine (SVM), general regression neural network (GRNN), and an ensemble learning: the random forest (RF) regression method. With the models, the low accuracy calculations, TDDFT calculated absorption energies (&#x03BB;<sub>max</sub>) for 433 organic molecules with the minimum basis set STO-3G, are significantly improved. The mean absolute error (MAE) and the root mean square error (RMSE) of the calculated &#x03BB;max are reduced from 0.62 and 0.79 eV to 0.11 and 0.14 eV, respectively. The validation parameters of the proposed correction model can reach up to R<sup>2</sup>(0.97), Q<sup>2</sup>(0.98), and Q<sub>cv</sub><sup>2</sup> (0.99), which suggests the great goodness-of-fit and predictability. This investigation illustrates that the proposed ensemble correction models by sophisticated algorithms are highly efficient and accurate. Therefore, it may serve as an alternative tool to establish good correction models for TDDFT absorption calculations, which could significantly improve the accuracy of TDDFT calculations and extend machine learning algorithms on other feature calculations of excited states.https://ieeexplore.ieee.org/document/8668753/Ensemble learningAdaBoostregressionTDDFTabsorption energies
collection DOAJ
language English
format Article
sources DOAJ
author Jingxia Cui
Wenze Li
Chao Fang
Shunting Su
Jiaoyang Luan
Ting Gao
Lihong Hu
Yinghua Lu
Guanhua Chen
spellingShingle Jingxia Cui
Wenze Li
Chao Fang
Shunting Su
Jiaoyang Luan
Ting Gao
Lihong Hu
Yinghua Lu
Guanhua Chen
AdaBoost Ensemble Correction Models for TDDFT Calculated Absorption Energies
IEEE Access
Ensemble learning
AdaBoost
regression
TDDFT
absorption energies
author_facet Jingxia Cui
Wenze Li
Chao Fang
Shunting Su
Jiaoyang Luan
Ting Gao
Lihong Hu
Yinghua Lu
Guanhua Chen
author_sort Jingxia Cui
title AdaBoost Ensemble Correction Models for TDDFT Calculated Absorption Energies
title_short AdaBoost Ensemble Correction Models for TDDFT Calculated Absorption Energies
title_full AdaBoost Ensemble Correction Models for TDDFT Calculated Absorption Energies
title_fullStr AdaBoost Ensemble Correction Models for TDDFT Calculated Absorption Energies
title_full_unstemmed AdaBoost Ensemble Correction Models for TDDFT Calculated Absorption Energies
title_sort adaboost ensemble correction models for tddft calculated absorption energies
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description Molecular excited states are important for molecular optical properties, which can be feasibly explored by quantum chemical calculations. However, the computation is highly demanding due to their complicated characteristic features. Therefore, high accuracy and unambiguous descriptions are strongly desired for excited state investigations. This paper proposes accurate, robust, and efficient ensemble correction models for absorption calculations with the most used quantum chemical method, time-dependent density functional theory (TDDFT). Models are built by AdaBoost framework with both weak machine learning: support vector machine (SVM), general regression neural network (GRNN), and an ensemble learning: the random forest (RF) regression method. With the models, the low accuracy calculations, TDDFT calculated absorption energies (&#x03BB;<sub>max</sub>) for 433 organic molecules with the minimum basis set STO-3G, are significantly improved. The mean absolute error (MAE) and the root mean square error (RMSE) of the calculated &#x03BB;max are reduced from 0.62 and 0.79 eV to 0.11 and 0.14 eV, respectively. The validation parameters of the proposed correction model can reach up to R<sup>2</sup>(0.97), Q<sup>2</sup>(0.98), and Q<sub>cv</sub><sup>2</sup> (0.99), which suggests the great goodness-of-fit and predictability. This investigation illustrates that the proposed ensemble correction models by sophisticated algorithms are highly efficient and accurate. Therefore, it may serve as an alternative tool to establish good correction models for TDDFT absorption calculations, which could significantly improve the accuracy of TDDFT calculations and extend machine learning algorithms on other feature calculations of excited states.
topic Ensemble learning
AdaBoost
regression
TDDFT
absorption energies
url https://ieeexplore.ieee.org/document/8668753/
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