Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost
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d2sc00393g.pdf
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900.75 KB
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Checksum (MD5)
19d540220f1a678f676a5b952ef86d1d
Author(s)
Duan, Chenru
Chu, Daniel B. K.
Nandy, Aditya
Kulik, Heather J.
Date Issued
2022
Publisher
Royal Society of Chemistry (RSC)
Citation
Duan, Chenru, Chu, Daniel B. K., Nandy, Aditya and Kulik, Heather J. 2022. "Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost." 13 (17).
Version
Final published version
Abstract
We demonstrate that cancellation in multi-reference effect outweighs accumulation in evaluating chemical properties. We combine transfer learning and uncertainty quantification for accelerated data acquisition with chemical accuracy.
Subjects
General Chemistry
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Creative Commons Attribution NonCommercial License 3.0
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DOI of Published Version
https://doi.org/10.1039/d2sc00393g
