AIMC Topic: Solar Energy

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Data Mining and Machine Learning Tools for Combinatorial Material Science of All-Oxide Photovoltaic Cells.

Molecular informatics Mar 20, 2015
Growth in energy demands, coupled with the need for clean energy, are likely to make solar cells an important part of future energy resources. In particular, cells entirely made of metal oxides (MOs) have the potential to provide clean and affordable...
Machine Learning Metals Models, Theoretical Oxides Data Mining Solar Energy
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Generating a vast chemical space for high polar surface area triphenylamine polymers by machine learning-DFT calculations assisted reverse engineering for photovoltaics.

Journal of molecular graphics & modelling Sep 1, 2025
The total polar surface area (TPSA) is a crucial parameter in photovoltaic (PV) materials, as it directly influences their solubility, processability, and device performance. This study leverages machine learning-assisted reverse engineering to gener...
Machine Learning Algorithms Density Functional Theory Models, Molecular Polymers Aniline Compounds Solar Energy
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