Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations

TL;DR

MALA is a scalable machine learning framework that accelerates electronic structure calculations in large-scale atomistic simulations, enabling efficient modeling of complex materials beyond traditional DFT methods.

Contribution

The paper introduces MALA, a novel scalable ML package that integrates data sampling, training, and inference for electronic structure predictions compatible with standard DFT codes.

Findings

Successfully predicts electronic properties of large systems

Demonstrates computational efficiency and scalability

Applicable to diverse material systems

Abstract

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.