perovskite

Comparative Analysis of Machine Learning Approaches on the Prediction of the Electronic Properties of Perovskite: A Case Study of the ABX3 and A2BB’X6

Monte Carlo codes to simulate polar domains in hybrid perovskite solar cells

A set of tools and structures for modelling perovskite polytypes

A Python-based Finite Difference Multi-dimensional Electro-ionic Drift Diffusion Simulator for Perovskite Solar Cells

machine learning for the buried-interface engineering by organic modifiers

This is one tool to generator perovskite structures for calculation, such as First-principles calculations.

Python simulation toolkit for perovskite-silicon tandem solar cell modules. Covers I-V modelling, terminal configurations (2T/3T/4T), cell-to-module (CTM) loss analysis, and bypass diode protection under partial shading.

Perovskite solar cell drift-diffusion simulator: Python physics core (TMM optics, ion migration, selective contacts), FastAPI streaming backend, and Vite/TypeScript frontend with an interactive Layer Builder.

A physics-based solar farm simulator.

LLM Agent & Knowledge Graph for PV Solar Materials Science — RDF/OWL · SPARQL · ReAct · Groq

Python toolkit for analyzing mixed-cation perovskites (e.g., Cs0.5A0.5PbI3). Features unique structure generation, bond/strain analysis, tolerance factor calculation, and UV-Vis calibration. Aids research on cation arrangement effects on optoelectronic properties & stability.

A research project that transforms scientific literature on perovskite materials into structured, knowledge-graph-based representations for training LLMs focused on synthesis optimisation, experimental reproducibility, and discovery of novel materials knowledge.

PDF file, LaTeX sources and images of my PhD thesis

This repository hosts code designed to convert uxd-format files from an XRD machine into CSV files. These files are subsequently used for graphical representation and analysis of properties such as: FWHM, theta and crystallite size using the Debye-Scherrer and Williamson-Hall formulas. All of this is carried out using Python.

CLI that applies the Clever Materials falsification test to materials-science ML datasets: detects confounding via a bibliographic fingerprint (year/author/journal leaking the target). Built on Jablonka 2026 (arXiv:2602.17730) + Huang&Cole / Nandy datasets (Scientific Data).

Reproducibility package for physics-constrained LLM-assisted symbolic regression of hybrid perovskite band gaps.

Ferroelectric-Perovskite Hybrid Solar Architecture with Phononic Mitigation

Calculating the pseudo spin phase transition in metal halide perovskites

Active-learning discovery of photovoltaic ABX₃ perovskites with physics-based screening, ML surrogates and GPAW/ASE DFT

Maximum Power Point Tracker developed by the Diamond Consortium