Machine Learning-Assisted Optimization of Original Electrosynthetic Methods for the Sustainable Valorization of Feedstock and bio-sourced chemicals

The project, “Machine Learning-Assisted Optimization of Original Electrosynthetic Methods for the Sustainable Valorization of Feedstock and Bio-sourced Chemicals,” seeks to develop the field of eco-responsible synthesis. By combining the power of machine learning with innovative electrosynthetic techniques, we aim to transform the way we produce organic building blocks from feedstock and bio-sourced chemicals. Electrosynthesis, a green chemistry approach relying on redox processes, holds immense potential for sustainable chemical production. However, its widespread adoption has been limited by the complexity of optimizing reaction conditions. We aim to bridge this gap by harnessing the capabilities of machine learning to swiftly and intelligently navigate the vast parameter space of electrosynthetic reactions. By applying advanced algorithms and predictive models, we will unlock the full potential of electrosynthesis, enabling precise control over product outcomes, minimizing waste, and reducing environmental impact. Our project will contribute to the eco-friendly production of organic chemicals, fostering the sustainable valorization of feedstock and bio-sourced chemicals. In summary, this research project is at the intersection of chemistry and artificial intelligence, promising a brighter, greener future for the chemical industry through innovative, eco-responsible synthesis methods.