materials-project

A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.

Use time-splits for Materials Project entries for generative modeling benchmarking.

[npj Computational Materials] The implementation for the paper "Physics-informed graph neural network representation learning for crystal property prediction"

Transfer learning using a high order message passing foundational model to F-Li-B cells.

Interactive phase diagram generator for up to 4 components (solid phases) using the Materials Project API and pymatgen.

Automatic Generation of Quantum ESPRESSO Input Files

AI-native materials discovery workspace with graph-grounded evidence and agentic candidate screening

Modern React UI library based on mp-react-components, for materials science applications, with full TypeScript support and reusable scientific UI components.

Some programs I use in my research

A composition-based ML framework for predicting electronic band gaps of inorganic materials. XGBoost & Random Forest on 15,537 Materials Project compounds using Magpie descriptors.

Scientific data enrichment tool for Open WebUI - Chemistry and materials science integration with PubChem, ChEMBL, Materials Project, and RDKit

Predicting lithium cathode properties (voltage, capacity, formation energy, stability) from crystal structure using CGCNN, M3GNet, TensorNet, Random Forest, and XGBoost on data from Materials Project, OQMD, AFLOW, and JARVIS

Multimodal Deep Learning pipeline for crystalline bandgap prediction. Combines 1D X-Ray Diffraction (XRD) patterns with engineered tabular features (Magpie & CrystalNN) using a dual-branch PyTorch ResNet architecture. Features high-throughput data extraction via Materials Project API and automated structural featurization.

Deep Learning toolkit for Materials Science. Predict properties (CGCNN, MEGNet, M3GNet), discover new materials (GNoME-inspired), simulate XRD, and serve via GraphQL API.

Criticality-aware sodium-ion cathode prioritization using Materials Project records, text-mined evidence, and leakage-audited ML.

Does crystal symmetry predict oxide stability? Four-part analysis on Materials Project data: exception detection, composite scoring, polymorph case studies, and solar band gap screening.

Independent reproduction of ECSG thermodynamic stability prediction with fixed Materials Project split, released checkpoints, and paper-vs-reproduction analysis.

Pourbaix GUI R4.0 — no-code Windows desktop app (PySide6) to generate Pourbaix (E-pH / pH–potential) diagrams for corrosion, electrochemical stability, and photoelectrode screening, using the Materials Project API via pymatgen. Interactive multi-element systems, tunable plot styling, export of images (PNG/TiFF/SVG) and boundary data (CSV/XLSX/TXT).

Provenance-first phase scouting and indexed powder diffraction references

ML pipeline that screens the Materials Project for overlooked solar absorbers — surfaced a validated 1.3 eV candidate with zero PV literature