The scientists have found a way to accelerate chemical simulations using artificial intelligence.
Credit: Copyright Philipp Marquetand
Drastic advances in research of artificial intelligence have led to a wide range of fascinating developments in this area over the last decade. Autonomously driven cars, but also everyday applications such as search engines and spam filters illustrate the versatility of methods from the field of artificial intelligence.
An international group of researchers led by Philipp Marquetand from the Faculty of Chemistry at the University of Vienna has now found a way to accelerate these simulations using artificial intelligence. For this purpose, so-called artificial neural networks are used, mathematical models of the human brain. These are able to learn the complex quantum mechanical relationships that are necessary for the modelling of infrared spectra by using only a few examples. In this way, the scientists can carry out simulations within a few minutes, which would otherwise take thousands of years even with modern supercomputers — without sacrificing reliability. “We can now finally simulate chemical problems that could not be overcome with the simulation techniques used up to now,” says Michael Gastegger, the first author of the study.
Based on the results of this study, the researchers are confident that their method of spectra prediction will be widely used in the analysis of experimental infrared spectra in the future.
Story Source: Materials provided by University of Chicago Original written by Whitney Clavin.Note: Content may be edited for style and length.
Michael Gastegger, Jörg Behler, Philipp Marquetand. Machine learning molecular dynamics for the simulation of infrared spectra. Chem. Sci., 2017; 8 (10): 6924 DOI: 10.1039/C7SC02267K