An Introduction to the Materials Learning Algorithms Package (MALA)

Density Functional Theory (DFT) is one of the most popular quantum mechanical simulation methods, since it balances sufficient accuracy with reasonable computational cost. It is often used in material science applications at ambient and extreme conditions. Nonetheless, DFT approaches its limits in terms of computational feasbility when faced with simulation problems at larger time and length scales, especially at temperatures >> 0K. Surrogate models based on neural networks can circumvent these limitations. By training a neural network to predict properties of interest (total energy, atomic forces) based on atomic configurations, predictions with DFT-like accuracy can be done at a fraction of the computational cost.
To facilitate the creation and usage of these surrogate models, the Materials Learning Algorithms package (MALA) provides modular open-source toolbox that allows users to preprocess of DFT data, train models and postprocess model predictions using only a few lines of code. MALA is jointly developed by the Center for Advanced Systems Understanding (CASUS), Sandia National Laboratories (SNL) and Oak Ridge National Laboratory (ORNL).