Machine Learning in Chemical Engineering
Knowledge Meets Data: Interpretability, Extrapolation, Reliability, Trust

Welcome to the homepage for the DFG priority programme 2331: Machine Learning in Chemical Engineering (SPP 2331).

Within SPP 2331, researchers from various fields – including chemical engineering, computer science, and mathematics – are teaming up to enable the chemical industry’s transition to renewable energy and raw materials by using machine learning as a catalyst.


Chemical data

Utilize chemical data with machine learning to catalyze transformation processes of chemical industry towards digitization & full automation.


Machine learning

Advance machine learning methods, e.g., deep learning or graph machine learning, and tailor them to real-world chemical engineering applications. Make machine learning usable by interpretability, extrapolation, reliability, and trust.


Research collaborations

Foster researchers from both chemical and machine learning community to collaborate in tandem projects and support young (female) researchers, e.g., PhD students, PostDocs, assistant professors.

Research projects

Find more information about the research projects within SPP 2331.


Recent and upcoming workshops, lectures, trainings, etc. within SPP 2331:

  • 03/2024: PI/PhD/PostDoc Workshop (Kaiserslautern)
  • 09/2023: PI/PhD/PostDoc Workshop (Berlin)
  • 03/2023: PhD/PostDoc Workshop (Berlin)