Scalable Machine Learning for Electronic Structure Calculations
We develop the Materials Learning Algorithms (MALA), a physics-informed machine learning framework that aims to accelerate conventional density functional theory simulations. Using neural networks, MALA efficiently computes the electronic structure of matter, enabling accurate determination of energies and forces that are critical for atomistic simulations. MALA is a scalable method that balances accuracy and speed, overcoming the limitations of conventional density functional theory simulations. It combines the scalability of atomistic simulations with the high accuracy of first-principles methods, paving the way for electronic structure calculations at unprecedented length and time scales. This advancement in materials modeling opens up a broad range of potential applications.
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Predicting the electronic structure across a stacking fault in a slab of Beryllium. This simulation includes over 100,000 atoms.
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Machine learning takes materials modeling into new era
The arrangement of electrons in matter, known as electronic structure, plays a critical role in fundamental and applied research such as drug design and energy storage. However, the lack of a simulation technique that provides both high fidelity and scalability across different time and length scales has long been an obstacle to the progress of these technologies. We have developed a machine learning method – the Materials Learning Algorithms (MALA) – that replaces traditional electronic structure simulation techniques. MALA enables electronic structure simulations at previously unattainable length scales.
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L. Fiedler, N. A. Modine, S. Schmerler, D. J. Vogel, G. A. Popoola, A. P. Thompson, S. Rajamanickam, A. Cangi, Predicting electronic structures at any length scale with machine learning, Npj Comput. Mater. 9, 115 (2023).