Neural networks make sense of complex electron interactions

Researchers from the Center for Materials Technologies at Skoltech have delivered a proof-of-concept demonstration of a neural network-driven method for creating a precise exchange-correlation functional interpolation, which is the core component of density functional theory. DFT, in turn, is the main numerical method used in condensed matter physics and quantum chemistry to calculate compound reactivity, the zonal structure of molecules, the durability of materials, and other properties crucial for the search for new materials, drugs, and more. The promising neural network architecture was presented and analyzed in Scientific Reports.

from News on Artificial Intelligence and Machine Learning https://ift.tt/b71GUa8

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