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

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

<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>

Bibliographic Details
Main Authors: Duan, Chenru, Chu, Daniel B. K., Nandy, Aditya, Kulik, Heather J.
Other Authors: Massachusetts Institute of Technology. Department of Chemical Engineering
Format: Article
Published: Royal Society of Chemistry (RSC) 2022
Subjects:
General Chemistry
Online Access:https://hdl.handle.net/1721.1/146548

Internet

https://hdl.handle.net/1721.1/146548

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