Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

Author(s)
Duan, Chenru; Chu, Daniel B. K.; Nandy, Aditya; Kulik, Heather J.
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Abstract
<jats:p>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.</jats:p>
Date issued
2022
URI
https://hdl.handle.net/1721.1/146548
Department
Massachusetts Institute of Technology. Department of Chemical Engineering; Massachusetts Institute of Technology. Department of Chemistry
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
ISSN
2041-6520
2041-6539
Keywords
General Chemistry
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