Molecular machine learning in chemical process design

Current Opinion in Chemical Engineering, vol. 52, pp. 101239

Abstract

Molecular machine learning (ML) has recently demonstrated great potential in (i) predicting properties of pure components and their mixtures, and (ii) exploring the chemical space. We review state-of-the-art molecular ML models, such as graph neural networks and transformers, and discuss research directions for further advancements in chemical process engineering. This includes leveraging molecular ML at the process scale, for example, in process design and optimization formulations, which promises to accelerate the identification of novel molecules and processes. To this end, it will be essential to create design benchmarks and practically validate proposed candidates, possibly in collaboration with the chemical industry.

Authors 5

1. Jan G. Rittig Aachen Process Systems Engineering (AVT.SVT)

   École Polytechnique Fédérale de Lausanne · RWTH Aachen University

   Affiliation as printed

   Laboratory of Artificial Chemical Intelligence (LIAC), Institute of Chemical Sciences and Engineering, EPFL, Lausanne, Switzerland

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany

2. Manuel Dahmen

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems ICE-1: Energy Systems Engineering, Jülich, Germany

3. Martin Grohe Aachen Lehrstuhl Informatik 7

   RWTH Aachen University

   Affiliation as printed

   Lehrstuhl Informatik 7, RWTH Aachen University, Aachen, Germany

4. Philippe Schwaller

   NCCR Catalysis · École Polytechnique Fédérale de Lausanne

   Affiliation as printed

   Laboratory of Artificial Chemical Intelligence (LIAC), Institute of Chemical Sciences and Engineering, EPFL, Lausanne, Switzerland

   National Centre of Competence in Research (NCCR) Catalysis, EPFL, Lausanne, Switzerland

5. Alexander Mitsos corresponding Aachen Process Systems Engineering (AVT.SVT)

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems ICE-1: Energy Systems Engineering, Jülich, Germany

   JARA Center for Simulation and Data Science (CSD), Aachen, Germany

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany