JingChun Wang, Yao Wang, Rui-Xue Xu, GuanHua Chen & Xiao Zheng. (2023) A semilocal machine-learning correction to density functional approximations. The Journal of Chemical Physics 158:15.
Crossref
Pavlo O. Dral, Tetiana Zubatiuk & Bao-Xin Xue. 2023. Quantum Chemistry in the Age of Machine Learning. Quantum Chemistry in the Age of Machine Learning
491
507
.
Wenze Li, Jia Liu, Lin Li, LiHong Hu, Zhong-Min Su & GuanHua Chen. 2021. Computational Materials, Chemistry, and Biochemistry: From Bold Initiatives to the Last Mile. Computational Materials, Chemistry, and Biochemistry: From Bold Initiatives to the Last Mile
183
212
.
Pavlo O. Dral, Alec Owens, Alexey Dral & Gábor Csányi. (2020) Hierarchical machine learning of potential energy surfaces. The Journal of Chemical Physics 152:20.
Crossref
Pavlo O. Dral. (2020) Quantum Chemistry in the Age of Machine Learning. The Journal of Physical Chemistry Letters 11:6, pages 2336-2347.
Crossref
GuanYa Yang, Jiang Wu, ShuGuang Chen, WeiJun Zhou, Jian Sun & GuanHua Chen. (2018) Size-independent neural networks based first-principles method for accurate prediction of heat of formation of fuels. The Journal of Chemical Physics 148:24.
Crossref
Juan Wang, Xiaoyu Yang, Zhi Zeng, Xiaoli Zhang, Xushan Zhao & Zongguo Wang. (2017) New methods for prediction of elastic constants based on density functional theory combined with machine learning. Computational Materials Science 138, pages 135-148.
Crossref
Juan Wang, Xiaoyu Yang, Guisheng Wang, Jie Ren, Zongguo Wang, Xushan Zhao & Yue Pan. (2017) Error estimation in high-throughput density functional theory calculation for material property: elastic constants of cubic binary alloy case. Computational Materials Science 134, pages 190-200.
Crossref
Ting Gao, Hongzhi Li, Wenze Li, Lin Li, Chao Fang, Hui Li, LiHong Hu, Yinghua Lu & Zhong-Min Su. (2016) A machine learning correction for DFT non-covalent interactions based on the S22, S66 and X40 benchmark databases. Journal of Cheminformatics 8:1.
Crossref
Hong Zhi Li, Lin Li, Zi Yan Zhong, Yi Han, LiHong Hu & Ying Hua Lu. (2013) An Accurate and Efficient Method to Predict Y-NO Bond Homolysis Bond Dissociation Energies. Mathematical Problems in Engineering 2013, pages 1-10.
Crossref
Hong Zhi Li, Li Hong Hu, Wei Tao, Ting Gao, Hui Li, Ying Hua Lu & Zhong Min Su. (2012) A Promising Tool to Achieve Chemical Accuracy for Density Functional Theory Calculations on Y-NO Homolysis Bond Dissociation Energies. International Journal of Molecular Sciences 13:7, pages 8051-8070.
Crossref
Hui Li, Ting Gao, Yinghua Lu, Hongzhi Li & Zhongmin Su. (2011) Combined Density Functional Theory and Ensembled Elman Network Correction Approach for Electronic Excitation Energies. Combined Density Functional Theory and Ensembled Elman Network Correction Approach for Electronic Excitation Energies.
Hong Zhi Li, Wei Tao, Ting Gao, Hui Li, Ying Hua Lu & Zhong Min Su. (2011) Improving the Accuracy of Density Functional Theory (DFT) Calculation for Homolysis Bond Dissociation Energies of Y-NO Bond: Generalized Regression Neural Network Based on Grey Relational Analysis and Principal Component Analysis. International Journal of Molecular Sciences 12:4, pages 2242-2261.
Crossref
Roman M. Balabin & Ekaterina I. Lomakina. (2011) Support vector machine regression (LS-SVM)—an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?. Physical Chemistry Chemical Physics 13:24, pages 11710.
Crossref
Ting Gao, Hong-Zhi Li & Ying-Hua Lu. (2010) Accurate prediction of heats of formation for c1-c16 alkanes: The genetic algorithm and neural network approach with simple input descriptors. Accurate prediction of heats of formation for c1-c16 alkanes: The genetic algorithm and neural network approach with simple input descriptors.
Hui Li, Jianan Wang, Ting Gao, Yinghua Lu & Zhongmin Su. (2010) Accurate Prediction of the Optical Absorption Energies by Neural Network Ensemble Approach. Accurate Prediction of the Optical Absorption Energies by Neural Network Ensemble Approach.
Ting Gao, Dong-Bing Pu, Hui Li, Ying-Hua Lu, Hai-Bin Li, Hong-Zhi Li & Zhong-Min Su. (2010) Improving the Accuracy of Low Level Density Functional Theory Calculation for Absorption Energies: The Least Squares Support Vector Machine. Improving the Accuracy of Low Level Density Functional Theory Calculation for Absorption Energies: The Least Squares Support Vector Machine.
Roman M. Balabin & Ekaterina I. Lomakina. (2009) Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies. The Journal of Chemical Physics 131:7.
Crossref
Ting Gao, Shi-Ling Sun, Li-Li Shi, Hui Li, Hong-Zhi Li, Zhong-Min Su & Ying-Hua Lu. (2009) An accurate density functional theory calculation for electronic excitation energies: The least-squares support vector machine. The Journal of Chemical Physics 130:18.
Crossref
Ting Gao, Li-Li Shi, Hai-Bin Li, Shan-Shan Zhao, Hui Li, Shi-Ling Sun, Zhong-Min Su & Ying-Hua Lu. (2009) Improving the accuracy of low level quantum chemical calculation for absorption energies: the genetic algorithm and neural network approach. Physical Chemistry Chemical Physics 11:25, pages 5124.
Crossref
Hui Li, LiLi Shi, Min Zhang, Zhongmin Su, XiuJun Wang, LiHong Hu & GuanHua Chen. (2007) Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach. The Journal of Chemical Physics 126:14.
Crossref
David A. Long & James B. Anderson. (2005) Bond-based corrections to semi-empirical and ab initio electronic structure calculations. Chemical Physics Letters 402:4-6, pages 524-528.
Crossref
Xiao Zheng, LiHong Hu, XiuJun Wang & GuanHua Chen. (2004) A generalized exchange-correlation functional: the Neural-Networks approach. Chemical Physics Letters 390:1-3, pages 186-192.
Crossref