Software and tools

Research software for materials discovery.

Open tools, foundation models, and reusable workflows that connect materials data, molecular representations, uncertainty-aware modeling, and polymer design.

Open-source package Active development

Physics-informed Gaussian Process Regression for materials informatics

matgpr

A public Python package for reproducible materials-informatics workflows with Gaussian Process Regression, uncertainty-aware prediction, materials fingerprints, and physics-informed mean functions.

GitHub Documentation

Supports scikit-learn and GPyTorch GPR workflows with validation, learning curves, uncertainty diagnostics, and plotting utilities.
Includes physics-aware kernels, target transforms, virtual-observation constraints, derivative-constrained models, and reusable materials-physics equation templates.
Designed as a practical platform for future tutorials and blog posts on physics-informed GPR, Bayesian optimization, and small-data materials modeling.

Research software Paper published

Foundation language model for generative polymer design

POLYT5

An encoder-decoder chemical language model that predicts polymer properties and generates new polymer structures conditioned on target performance.

npj Artificial Intelligence

Generates chemically valid, synthesizable polymer candidates without exhaustive enumeration.
Applied to dielectric polymer discovery with over 20,000 promising candidates.
Connected with a general-purpose LLM for natural-language property prediction and design.

Open-source package Paper published

Autonomous atomic-scale polymer model generation

Polymer Structure Predictor

A Python toolkit that builds a hierarchy of polymer models from repeat-unit SMILES, including oligomers, infinite chains, crystals, and amorphous structures.

GitHub JCTC article

Generates structures and force-field files for downstream simulations.
Supports workflows in VASP, ORCA, LAMMPS, and GAMESS.
Open-source package designed to make polymer simulations more autonomous.