  We propose a novel method enabling autocompletion of chemical flowsheets.
This idea is inspired by the autocompletion of text. We represent flowsheets as
strings using the text-based SFILES 2.0 notation and learn the grammatical
structure of the SFILES 2.0 language and common patterns in flowsheets using a
transformer-based language model. We pre-train our model on synthetically
generated flowsheets to learn the flowsheet language grammar. Then, we
fine-tune our model in a transfer learning step on real flowsheet topologies.
Finally, we use the trained model for causal language modeling to autocomplete
flowsheets. Eventually, the proposed method can provide chemical engineers with
recommendations during interactive flowsheet synthesis. The results demonstrate
a high potential of this approach for future AI-assisted process synthesis.
