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>
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Published: |
Royal Society of Chemistry (RSC)
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/1721.1/146548 |