Generative Discovery of Novel Chemical Designs using Diffusion Modeling and Transformer Deep Neural Networks with Application to Deep Eutectic Solvents

Understand

We report a series of deep learning models to solve complex forward and inverse design problems in molecular modeling and design.

• Using both diffusion models inspired by nonequilibrium thermodynamics and attention-based transformer architectures, we demonstrate a flexible framework to capture complex chemical structures.
• First trained on the QM9 dataset and a series of quantum mechanical properties (e.g.
• homo, lumo, free energy, heat capacity, etc.), we then generalize the model to study and design key properties of deep eutectic solvents.