This article highlights a breakthrough in computational materials science, where machine learning significantly accelerates the prediction of materials’ spectral properties. Using Koopmans functionals, which enhance density-functional theory (DFT) to predict light absorption and other spectral characteristics, researchers often face challenges in calculating complex “screening parameters.” These parameters indicate how electrons in a material respond to adding or removing an electron, critical for applications like solar cells. In a study involving liquid water and the halide perovskite CsSnI3, researchers demonstrated that a simple machine learning model, ridge regression, could predict these parameters accurately and efficiently, reducing computational costs. Despite its simplicity, the success was attributed to the careful design of descriptors capturing the system’s physics. This approach opens the door to faster, more efficient exploration of temperature-dependent spectral properties in materials.
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Many thanks for your interest and consideration,
Dr. Gabriele Mogni
Technical Consultant and EU Representative of Virtual Lab Inc., the parent company of the Materials Square platform
Website: Home | Virtual Lab Inc.
Email: gabriele@simulation.re.kr
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