Physics-informed machine learning for photocatalyst discovery.
A physics-informed Random Forest and XGBoost ensemble that ranks MOF/g-C3N4 Type-II heterojunction candidates for photocatalytic hydrogen production without exhaustive hybrid-functional calculations.
- Journal
- Chinese Journal of Physics
- Volume
- 103 · 1881-1895
- DOI
- 10.1016/j.cjph.2026.07.025
Physics-informed machine learning screening and validation of metal-organic framework/g-C3N4 heterojunction photocatalysts.
The study addresses a costly materials-discovery problem: identifying metal-organic frameworks that can form useful Type-II heterojunctions with graphitic carbon nitride for photocatalytic hydrogen production. It combines physical band-alignment rules with interpretable machine learning to prioritize candidates before high-cost quantum-mechanical validation.
- Authors
- Abdullah Khan · Muhammad Saeed · Fakhrud Din · Farhat Ullah · Sami Ullah
- Farhat Ullah's contribution
- Methodology, formal analysis, validation, visualization, writing the original draft, and review and editing.
What the paper contributes.
- 01
Physics-grounded features
Six interpretable descriptors encode metal-node chemistry, linker electronics and band alignment, reducing dependence on costly per-structure quantum calculations.
- 02
Leakage-resistant validation
Chemical fingerprints isolate 17 experimentally characterized MOF families as external holdouts instead of letting closely related structures leak into training.
- 03
A hybrid decision pipeline
Random Forest and XGBoost rankings are combined with thermodynamic, pore-accessibility and stability constraints to produce an experimentally useful shortlist.
A shorter path to credible candidates.
The consensus ensemble was tested internally and against isolated experimental families. The final Physics-ML cascade traded a small amount of global recall for much stronger precision at the top of the ranking, where experimental resources would actually be spent.
- MOFs analyzed
- 20,152Integrated QMOF and CoRE-MOF dataset
- Ensemble ROC-AUC
- 0.912On 4,029 internal test structures
- Top-50 precision
- 98%49 of 50 candidates were true positives
- Final shortlist
- 983From 20,152 candidates
- Compute reduction
- 20.5xVersus exhaustive HSE06 screening
- External MOF families
- 1740 experimentally characterized structures
The validated framework is scoped to closed-shell and main-group metal nodes. Open-shell 3d transition-metal systems require higher-fidelity band-gap modeling, and every shortlisted material still needs prospective quantum and experimental validation.
Have a system that needs to work in production?
Tell me what's breaking — or what you're building.