DOE STTR Phase I Final Report Report: Machine-learning Based Prediction of Thermal Limits for Conjugated Organic Materials
The SCANN-DT Phase I STTR project led by NLM Photonics and partnering with the National Renewable Energy Laboratory (NREL) sought to apply machine learning techniques based on graph neural networks (GNNs) towards the prediction of decomposition temperatures (Td) of organic semiconductors, based on prior work on bond dissociation energy (BDE) prediction as implemented in NREL’s ALFABET prediction tool. Using a curated set of experimental decomposition energies, the project examined GNN-based, classical quantitative structure-property relationship (QSPR) based on DFT calculations, and combinations of both methods to predict Td. While the best MAEs in Td achieved were near 40°C, below project targets, the project led to improvements in the ALFABET model, improvements in cloud-based implementations of NWChem software, and a substantial dataset of calculations on medium-sized conjugated organic molecules.
- Research Organization:
- Nonlinear Materials Corporation DBA NLM Photonics
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Contributing Organization:
- National Renewable Energy Laboratory
- DOE Contract Number:
- SC0021579
- OSTI ID:
- 1995938
- Type / Phase:
- STTR (Phase I)
- Report Number(s):
- DOE-NLM-0021579
- Country of Publication:
- United States
- Language:
- English
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