Materials Learning Algorithms
MALA is a data-driven framework to generate surrogate models of density functional theory calculations based on machine learning. Its purpose is to enable multiscale modeling by bypassing computationally expensive steps in state-of-the-art density functional simulations.
MALA is designed as a modular and open-source python package. It enables users to perform the entire modeling toolchain using only a few lines of code. MALA is jointly developed by the Sandia National Laboratories (SNL) and the Center for Advanced Systems Understanding (CASUS).
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