mala-project/mala
Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.
This tool helps materials scientists and researchers predict material properties faster and more efficiently. It takes data from first-principles simulations, specifically density functional theory (DFT) calculations, and generates machine learning models. The output is a surrogate model that can rapidly predict material behavior, bypassing computationally intensive simulations. Materials scientists, computational chemists, and condensed matter physicists would find this useful for multiscale modeling.
Use this if you need to accelerate your materials science research by replacing expensive density functional theory (DFT) calculations with fast, machine-learned predictions.
Not ideal if you are not working with first-principles simulation data or need to perform full, high-fidelity DFT calculations for fundamental research.
Stars
98
Forks
27
Language
Python
License
BSD-3-Clause
Category
Last pushed
Jan 05, 2026
Commits (30d)
0
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