Machine Learning Takes Materials Modeling Into New Era

Deep learning approach enables accurate electronic structure calculations at large scales

Copyright: chemeurope.com – “Machine Learning Takes Materials Modeling Into New Era”

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The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research such as drug design and energy storage. However, the lack of a simulation technique that offers both high fidelity and scalability across different time and length scales has long been a roadblock for the progress of these technologies. Researchers from the Center for Advanced Systems Understanding (CASUS) at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) in Görlitz, Germany, and Sandia National Laboratories in Albuquerque, New Mexico, USA, have now pioneered a machine learning-based simulation method (npj Computational Materials) that supersedes traditional electronic structure simulation techniques. Their Materials Learning Algorithms (MALA) software stack enables access to previously unattainable length scales.

Electrons are elementary particles of fundamental importance. Their quantum mechanical interactions with one another and with atomic nuclei give rise to a multitude of phenomena observed in chemistry and materials science. Understanding and controlling the electronic structure of matter provides insights into the reactivity of molecules, the structure and energy transport within planets, and the mechanisms of material failure.

Scientific challenges are increasingly being addressed through computational modeling and simulation, leveraging the capabilities of high-performance computing. However, a significant obstacle to achieving realistic simulations with quantum precision is the lack of a predictive modeling technique that combines high accuracy with scalability across different length and time scales. Classical atomistic simulation methods can handle large and complex systems, but their omission of quantum electronic structure restricts their applicability. Conversely, simulation methods which do not rely on assumptions such as empirical modeling and parameter fitting (first principles methods) provide high fidelity but are computationally demanding. For instance, density functional theory (DFT), a widely used first principles method, exhibits cubic scaling with system size, thus restricting its predictive capabilities to small scales.

Hybrid approach based on deep learning

The team of researchers now presented a novel simulation method called the Materials Learning Algorithms (MALA) software stack. In computer science, a software stack is a collection of algorithms and software components that are combined to create a software application for solving a particular problem. Lenz Fiedler, a Ph.D. student and key developer of MALA at CASUS, explains, “MALA integrates machine learning with physics-based approaches to predict the electronic structure of materials. It employs a hybrid approach, utilizing an established machine learning method called deep learning to accurately predict local quantities, complemented by physics algorithms for computing global quantities of interest.”

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