08.02.2024

DFG: Priority Programme - Machine Learning in Chemical Engineering. Knowledge Meets Data: Interpretability, Extrapolation, Reliability, Trust

Deadline: 4 June 2024

In 2020, the Senate of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) established the Priority Programme “Machine Learning in Chemical Engineering. Knowledge Meets Data: Interpretability, Extrapolation, Reliability, Trust” (SPP 2331). The programme brings together the chemical engineering (CE) and machine learning (ML) communities. By teaming chemical engineers with mathematicians and/or computer scientists, progress in all disciplines has been achieved over the first funding period of three years. The programme is designed to run for six years.

The present call invites proposals for the second three-year funding period starting in early 2025. Both continuation projects as well as new project proposals are highly encouraged. Each proposal must operate at the interface of CE and ML and have at least two applicants with corresponding expertise. The projects shall consider at least one of six scientific challenges: #1 optimal decision making, #2 introducing / enforcing physical laws in ML models, #3 heterogeneity of data, #4 information and knowledge representation, #5 safety and trust in ML applications and #6 creativity. The projects will be organised in a matrix between the areas of CE and the ML tasks. Data, models and methods will be shared among all participants of the programme on an internal platform. The organisation matrix and further information can be found on the homepage of the Priority Programme (see below).

The projects are expected to open up new methods for CE, formulate new types of problems for ML and jointly generate advances for methods in both ML and CE. Since ML has been used within CE for several years and substantial progress was made within the SPP 2331, projects shall go well beyond this state of the art. Under the umbrella of the six scientific challenges, the collaborative projects shall have promise for progress in process synthesis (especially regarding feedstock transformation), process flexibility, material selection, generation of alternatives and uncovering hidden information. Projects should address at least one CE area and one ML area, and clearly state how it will achieve progress in at least one of the challenges #1 to #6. Projects investigating and comparing different methods from ML for the same field of the collaboration matrix are particularly encouraged. Similarly, projects are encouraged where outcomes are transferable between the CE areas. Projects focusing on the phenomena and unit operation level should illustrate their implications on the process level.

The focus of the SPP 2331 projects shall be on the field of fluid processes with or without chemical reactions. Examples or products from other fields could be included in case the fluid process remains the focus. Reflecting the scientific challenges and needs of fluid processes, relatively broad CE methods are allowed, ranging from molecular modelling, thermodynamic calculations, reactor development and the prediction of fluid properties up to methods dedicated to operation, synthesis and design of whole processes (including control and optimisation, uncertainty quantification and optimal experimental design). Projects may be purely computational and/or have ML methods directly applied on experimental CE. Topics reaching beyond this scope may be included, provided they contain sufficient work on the methods above.

Proposals must be written in English and submitted to the DFG by 4 June 2024. 

The DFG strongly welcomes proposals from researchers of all genders and sexual identities, from different ethnic, cultural, religious, ideological or social backgrounds, from different career stages, types of universities and research institutions, and with disabilities or chronic illness. With regard to the subject-specific focus of this call, the DFG encourages female researchers in particular to submit proposals.

Further Information:
More information on the Priority Programme is available under:
https://www.dfg.de/de/aktuelles/neuigkeiten-themen/info-wissenschaft/2024/ifr-24-12
And
https://chemengml.org 

For scientific enquiries, please contact the Priority Programme coordinator:
Professor Alexander Mitsos
Rheinisch-Westfälische Technische Hochschule Aachen
Fakultät für Maschinenwesen
Aachener Verfahrenstechnik – Systemverfahrenstechnik (SVT)
Forckenbeckstraße 51
52074 Aachen
phone +49 241 8094704
alexander.mitsosspam prevention@avt.rwth-aachen.de 

Questions on the DFG proposal process can be directed to:
Programme contact: 
Dr. Simon Jörres
phone +49 228 885-2971
simon.joerresspam prevention@dfg.de

Administrative contact: 
Silke Stieber
phone +49 228 885-2687
silke.stieberspam prevention@dfg.de