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Photocatalytic Substrate Engineering through Ensemble Deep Learning

CORDIS · observation · Publication date unknown

Photocatalytic Substrate Engineering through Ensemble Deep Learning Over the past two decades, visible-light photocatalysis has revolutionized greener and more efficient chemical synthesis, significantly impacting pharmaceuticals, agrochemicals, and materials. However, a key challenge remains: the need to discover new substrates that can reliably and sustainably harness light energy. While current research has improved the prediction of light-driven reactions, existing methods are often slow, costly, and rely heavily on trial-and-error experimentation. The PROJECT, PHOTO-SEED, aims to pioneer an interdisciplinary approach, including Chemistry, Computer/Data Science, and Chemical Informatics. The project will: (a) create the first high-quality database of light-sensitive molecular properties, (b) develop deep learning models to provide accurate and reliable predictions across a wide range of molecules, (c) use generative AI to design synth

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recordType

award

status

SIGNED

region

EU

value

202125.12

unit

EUR