arXiv:2208.00859v2 Announce Type: replace Abstract: 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 flowsheet topologies to learn the flowsheet language grammar. Then, we fine-tune our model in a transfer learning step on real flowsheet topologies. Finally
The proliferation of advanced AI, especially transformer models, is enabling new applications in complex scientific and engineering domains, automating tasks previously considered intractable.
This development represents a significant step towards automating complex chemical and industrial design, potentially streamlining research and development in critical sectors.
Flowsheet design, a core process in chemical engineering, can now be partially automated by AI, reducing human effort and error while accelerating discovery and optimization.
- · Chemical engineering firms
- · Pharmaceutical companies
- · Materials science research
- · AI software developers
- · Manual flowsheet designers
- · Traditional CAD software vendors
Accelerated design and optimization of chemical processes leading to faster product development.
Reduced costs and increased efficiency in the chemical, energy, and materials industries.
Potential for entirely novel chemical processes and materials discovery facilitated by AI-driven exploration of design spaces.
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Read at arXiv cs.LG