  Knowledge of mixtures' phase equilibria is crucial in nature and technical
chemistry. Phase equilibria calculations of mixtures require activity
coefficients. However, experimental data on activity coefficients is often
limited due to high cost of experiments. For an accurate and efficient
prediction of activity coefficients, machine learning approaches have been
recently developed. However, current machine learning approaches still
extrapolate poorly for activity coefficients of unknown molecules. In this
work, we introduce the SMILES-to-Properties-Transformer (SPT), a natural
language processing network to predict binary limiting activity coefficients
from SMILES codes. To overcome the limitations of available experimental data,
we initially train our network on a large dataset of synthetic data sampled
from COSMO-RS (10 Million data points) and then fine-tune the model on
experimental data (20 870 data points). This training strategy enables SPT to
accurately predict limiting activity coefficients even for unknown molecules,
cutting the mean prediction error in half compared to state-of-the-art models
for activity coefficient predictions such as COSMO-RS, UNIFAC, and improving on
recent machine learning approaches.
