Publication: A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds
A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds
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Han, R., Rodríguez-Mayorga, M., & Luber, S. (2021). A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds. Journal of Chemical Theory and Computation, 17(2), 777–790. https://doi.org/10.1021/acs.jctc.0c00898
Abstract
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A proper treatment of electron correlation effects is indispensable for accurate simulation of compounds. Various post-Hartree–Fock methods have been adopted to calculate correlation energies of chemical systems, but time complexity usually prevents their usage in a large scale. Here, we propose a density functional approximation, based on machine learning using neural networks, which can be readily employed to produce results comparable to second-order Møller–Plesset perturbation (MP2) ones for organic compounds with reduced computat
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- Han, Ruocheng
- Rodríguez-Mayorga, Mauricio
- Luber, Sandra
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Han, R., Rodríguez-Mayorga, M., & Luber, S. (2021). A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds. Journal of Chemical Theory and Computation, 17(2), 777–790. https://doi.org/10.1021/acs.jctc.0c00898
