  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. In addition to separate forward and inverse models, we also
report an integrated fully prompt-based multi-task generative pretrained
transformer model that solves multiple forward, inverse design, and prediction
tasks, flexibly and within one model. We show that the multi-task generative
model has the overall best performance and allows for flexible integration of
multiple objectives, within one model, and for distinct chemistries, suggesting
that synergies emerge during training of this large language model. Trained
jointly in tasks related to the QM9 dataset and deep eutectic solvents (DESs),
the model can predict various quantum mechanical properties and critical
properties to achieve deep eutectic solvent behavior. Several novel
combinations of DESs are proposed based on this framework.
