Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost
Name
Semi-Supervised_Machine_Learning_Enables_the_Robust_Detection_of_Multireference_Character_at_Low_Cost_v1.pdf
Description
Submitted version
Size
12.03 MB
Format
Adobe PDF
Checksum (MD5)
6f6380f34f01dfd1c035cd48d7341067
Author(s)
Duan, Chenru
Liu, Fang
Nandy, Aditya
Kulik, Heather J
Date Issued
2020
Journal
Journal of Physical Chemistry Letters
Publisher
American Chemical Society (ACS)
Version
Original manuscript
Abstract
Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single-reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wave function theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. To reduce the cost of generating inputs to the VAT model, we leverage the VAT model's robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in an MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/ACS.JPCLETT.0C02018
