Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost
Chenru Duan, Daniel B. K. Chu, Aditya Nandy, Heather J. Kulik
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.
Topics & Concepts
ThroughputCluster (spacecraft)Character (mathematics)Computer scienceVirtual screeningTransfer of learningTransfer (computing)High-throughput screeningArtificial intelligenceChemistryComputer networkComputational chemistryTelecommunicationsParallel computingMathematicsBiochemistryWirelessMolecular dynamicsGeometryMachine Learning in Materials ScienceMachine Learning in Bioinformatics