Selected Projects
Six quick cards—click a title to see the full page with What · Why · How · Results . Github (Implementation details)
2D Linear Elasticity via PINN
Physics-informed NN for fast, physics-consistent field predictions.
PINNFEM val loss 2.22
Melting-Point Prediction (2-Level Ensemble)
Stacked RF/XGB/LGBM/MLP with SHAP-guided features.
R² ≈ 0.83~3.041k samples 2 level Ensemble
Oxidation-State Assignment
Soft-voting ensemble on lean OQMD/ICSD data.
Proxy R² ≈ 0.91~7k OQMD Weighted soft voting
Semantic Band-Gap (LLM + KG)
RDF/SPARQL schema with LLM-assisted, explainable queries.
RDF/SPARQLLLM Explainable
Materials Database App
Auto-ETL + Flask UI to track datasets → features → results.
Auto-ETLFlask + SQLite CRUD
Formation-Energy Prediction
AutoML baseline with Matminer features and MAE-first selection.
R² 0.971–0.998Matminer PyCaret