Md Habibur Rahman · School of Materials Engineering, Purdue University · [email protected] · [email protected] · msehabibur.github.io
If you use this library, please cite the associated papers.

DefectNet — Semiconductor Library

GitHub nanoHUB tool
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Defect thermodynamics

Breakdown of calculation outputs

Element-projected DOS

Formation energy vs Fermi level

Defect structure

Compound detail

Computed properties

Breakdown of calculation outputs

DFT-optimized structure

Stability — ΔGdecomp(T)

Prefer the nanoHUB route? The DefectDB simulation tool also runs live on nanoHUB (fine-tuned M3GNet force fields, CI-NEB barriers, finite-temperature MD): launch it there. MLFF results live in the calculator and the tool; every number in this library is pure DFT.

Data sources. All data presented on this site are taken from the following publications:

  1. M. H. Rahman and A. Mannodi-Kanakkithodi, Data-driven discovery of novel chalcogenide semiconductors for solar absorption, EES Solar (2026). 10.1039/D6EL00026F
  2. M. H. Rahman, M. Biswas, and A. Mannodi-Kanakkithodi, DeFecT-FF: a machine learning force field framework for high throughput defect modeling in CdTe-based solar cells, Phys. Chem. Chem. Phys. 28, 10718–10730 (2026). 10.1039/D6CP00170J
  3. M. H. Rahman, I. Agrawal, and A. Mannodi-Kanakkithodi, Using Machine Learning to Explore Defect Configurations in Cd/Zn–Se/Te Compounds, 2025 IEEE 53rd Photovoltaic Specialists Conference (PVSC), 2025. 10.1109/PVSC59419.2025.11133092
  4. M. H. Rahman and A. Mannodi-Kanakkithodi, Defect modeling in semiconductors: the role of first principles simulations and machine learning, J. Phys. Mater. 8, 022001 (2025). 10.1088/2515-7639/adb181
  5. M. H. Rahman and A. Mannodi-Kanakkithodi, High-throughput screening of ternary and quaternary chalcogenide semiconductors for photovoltaics, Comput. Mater. Sci. 249, 113654 (2025). 10.1016/j.commatsci.2024.113654
  6. M. H. Rahman, P. Gollapalli, P. Manganaris, S. K. Yadav, G. Pilania, B. DeCost, K. Choudhary, and A. Mannodi-Kanakkithodi, Accelerating defect predictions in semiconductors using graph neural networks, APL Mach. Learn. 2, 016122 (2024). 10.1063/5.0176333
  7. M. H. Rahman, M. Biswas, and A. Mannodi-Kanakkithodi, Understanding Defect-Mediated Ion Migration in Semiconductors using Atomistic Simulations and Machine Learning, ACS Mater. Au 4, 557–573 (2024). 10.1021/acsmaterialsau.4c00095
  8. M. H. Rahman, Y. Sun, and A. Mannodi-Kanakkithodi, High-throughput screening of single atom co-catalysts in ZnIn2S4, Mater. Adv. 5, 8673–8683 (2024). 10.1039/D4MA00616J
  9. M. H. Rahman, J. Yang, Y. Sun, and A. Mannodi-Kanakkithodi, Defect engineering in ZnIn2X4 (X = S, Se, Te) semiconductors for improved photocatalysis, Surf. Interfaces 39, 102960 (2023). 10.1016/j.surfin.2023.102960
  10. C. Han, G. Han, M. H. Rahman, A. Mannodi-Kanakkithodi, and Y. Sun, Photocatalytic Ketyl Radical Initiated Cketyl−Csp2/Csp3 Coupling on ZnIn2S4, Chem. Eur. J. 29, e202203785 (2023). 10.1002/chem.202203785
Calculation inputs
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