Researchers from Imperial and its spinout company SOLVE Chemistry have presented a chemical dataset at the prestigious AI conference NeurIPS that could help accelerate the use of machine learning to solve solvent challenges in industrial chemistry.
Industrial chemists often use prior data to help predict reaction outcomes such as how a certain solvent or temperature setting will perform in a manufacturing process. But existing datasets are patchy – for example, they typically only include certain solvents and certain temperatures. They are therefore not powerful enough to reliably predict the best way to produce a chemical.