Machine learning provides a shortcut to simulate interactions in materials for solar energy harvesting

Harnessing sunlight holds promise as a means to cleanly generate renewable energy for next-generation technologies, from solar fuel cells to water treatment systems. Such technologies require an understanding of what happens when materials and molecules absorb sunlight.

Computer simulations can help us better understand light-matter interactions. However, modeling materials featuring multiple types of structures, such as solid/water interfaces, is a complex task. But now, a research team at the U.S. Department of Energy’s (DOE) Argonne National Laboratory has found a way to simplify these modeling tasks.

Using a data-driven approach based on machine learning, the team was able to simplify the solution of the quantum mechanical equations that describe how light is absorbed by a solid, liquid or molecule. Results of the research were recently published in Chemical Sciences.

“It is certainly not intuitive at first, but it turns out that machine learning techniques can be used for purposes much different than recognizing images or predicting consumer needs,” said Marco Govoni, co-author of the study and assistant scientist in Argonne’s Materials Science division.

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