• DOI:10.1021/acs.jctc.9b00884
Corpus ID: 202232777

Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning

@article{Cheng2019RegressionclusteringFI,
  title={Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning},
  author={Lixue Cheng and Nikola B. Kovachki and Matthew Welborn and T. Miller},
  journal={Journal of chemical theory and computation},
  year={2019}
}

• Lixue Cheng, Nikola B. Kovachki, +1 author T. Miller
• Published 2019
• Computer Science, Physics, Medicine
• Journal of chemical theory and computation

Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically with the amount of data and becomes a computational bottleneck for large training sets. In the… Expand

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