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End-to-end ML projects in energy materials science and applied chemistry; from experimental data to deployable models. Each project combines domain expertise with rigorous ML methodology.
Projects
ML-powered impedance analysis for perovskite single crystals
Predicted low-frequency EIS response of MAPbBr₃ single crystals, eliminating 55-minute measurements using a supervised ML model trained on 8,741 datapoints.
R² = 0.981 · ACS Appl. Mater. Interfaces 2023
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