COREMat Framework

COREMat (COmputable REgression for Materials) is a Python framework for automated machine learning on tabular materials-science datasets — alloy research, materials-property prediction, and similar regression workloads. It automates the full workflow behind a single configuration: data ingestion, preprocessing, nested-CV training with hyperparameter optimisation, iterative feature selection, SHAP-based explainability, OOF/Hold-out evaluation, and interactive HTML reporting. The model registry ships 36 models (gradient boosting, kernel methods, linear models, neural nets, foundation models) behind a unified ModelSpec interface.

What You Get

Every trained model gets a self-contained, interactive HTML report — open it in a browser, no server or notebook required — and coremat eval or coremat report compare adds a comparison report over all models of a run. Excerpt of what's inside:

Every report is built post-hoc from persisted per-round predictions — re-opening a report never re-runs a model.

Installation

Requirements: Python >= 3.12, < 3.13

From PyPI (recommended):

uv add coremat          # in a uv project
# or:
pip install coremat

From GitLab (latest main):

uv pip install git+https://gitlab.uni-rostock.de/cs1302/coremat.git@main

Developers (editable install with the dev tools):

git clone https://gitlab.uni-rostock.de/cs1302/coremat.git
cd coremat
uv sync                      # creates .venv from uv.lock
uv run coremat build-data    # example datasets into data/

TabPFN Authentication

TabPFN models require a free PriorLabs account. Set up once after installation:

coremat auth tabpfn

Getting Started

The documentation (mkdocs) is the single source of truth for usage — it covers the full quick-start walkthrough, sample data generation, and the resulting artifact layout. Start at docs/index.md or the Walkthrough — the end-to-end tour in Python, each step with a copy-paste YAML equivalent.

Documentation

The documentation is published on Read the Docs. To preview it locally: uv run mkdocs serve (details in docs/development.md).

Citing COREMat

If you use COREMat in your research, please cite it. Machine-readable citation metadata is provided in CITATION.cff at the root of this repository.

Acknowledgements

This project was developed using an agentic AI-assisted workflow with Claude Code by Anthropic.