DeFecT-FF: a machine learning force field framework for high throughput defect modeling in CdTe-based solar cells
Abstract
accelerated prediction and screening, we identified many new low energy defect configurations and obtained high-fidelity defect formation energy diagrams using HSE06 calculations with spin-orbit coupling. The DeFecT-FF framework has been released publicly as an online tool on the nanoHUB platform, allowing users to upload any crystallographic information file, generate defects of interest, and compute defect formation energies as a function of Fermi level and chemical potential conditions, thus bypassing expensive DFT calculations.
Fields of Study & Subjects
Chalcogenide Semiconductor Thin Films
Machine Learning in Materials Science
Quantum Dots Synthesis And Properties
Publication details
- Journal / source
- Physical Chemistry Chemical Physics
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 28
- Issue
- 17
- Pages
- 10718–10730
- Publication date
- 1 January 2026
- Language
- EN