Reorganization Energy Predictions with Graph Neural Networks Informed by Low-Cost Conformers
Author:
Affiliation:
1. Department of Chemistry, Texas A&M University, College Station, Texas 77843, United States
Funder
Welch Foundation
Texas A and M University
Division of Materials Research
Publisher
American Chemical Society (ACS)
Subject
Physical and Theoretical Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.2c09030
Reference33 articles.
1. Charge Transport in Molecular Materials: An Assessment of Computational Methods
2. Applying Marcus theory to describe the carrier transports in organic semiconductors: Limitations and beyond
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4. De Novo Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen
5. Machine learning to accelerate screening for Marcus reorganization energies
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