AI reduces computational time required to study fate of molecules exposed to light

Light-induced processes are critical in transformative technologies such as solar energy harvesting, as well as in photomedicine and photoresponsive materials. Theoretical studies of the dynamics of photoinduced processes require numerous electronic structure calculations, which are computationally expensive. Scientists developed machine learning-based algorithms, which reduce these computations significantly.
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