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Workshop Summary

Chemistry, as one of the basic natural sciences, defines the fundamental concepts for many other fields, like medicine, pharmacy, and biotechnology, whereas chemical engineering deals with the technical realization of these concepts in (industrial) processes. Both fields have, over centuries, acquired an exceptionally deep and broad body of domain knowledge, including fundamental natural laws, sophisticated mechanistic models, and rich human expertise and experience. However, machine learning (ML) is rapidly transforming research in these fields: representation learning, graph neural networks, and transformers are already outperforming the best established models for predicting thermodynamic properties, reinforcement learning enables the identification of new attractive process routes, and ML force fields accelerate molecular simulation beyond what was possible before. While leading conferences and journals in these fields receive increasing numbers of contributions employing ML algorithms, there has been little exchange between domain experts and the core ML research community so far. In contrast to fields like biology, physics, or medicine, the core problems from chemistry and chemical engineering are relatively unknown in the ML community, with molecular design being one of the few exceptions.

Chemistry and chemical engineering offer exceptional challenges for developing ML models. Data are often sparse, heterogeneous, correlated, and often have high uncertainties, not to mention that they are frequently unstructured. Nevertheless, there are exceptionally high demands on ML methods in chemistry and chemical engineering. Models must be extremely reliable: if a chemical reaction or plant does not behave as predicted, this can have disastrous consequences for humans and the environment. They must also be interpretable and explainable by humans to be accepted in practical applications. Chemistry and chemical engineering provide a vast body of physical and chemical domain knowledge, ranging from (strict) laws of nature and boundary conditions to (soft) empirical correlations, and human experience. Therefore, an increasing number of hybrid (ML + domain knowledge) models are being developed in these fields.

To sum up, chemistry and chemical engineering offer a unique set of problems, applications, datasets, and unparalleled domain knowledge that simultaneously require and enable the development of specialized ML methods. Surprisingly, there has never been a workshop on a premier ML conference at the intersection of these fields so far. By fostering communication and interdisciplinary research in this area, we aim to close this gap, enrich both fields and allow a broader community to work on the problems necessary to chemistry and chemical engineering. As the first workshop of this type, we keep its scope broad on purpose: fields of applications might include property prediction, force field development, molecule discovery and design, equation discovery, or automation and anomaly detection in chemical processes. However, we explicitly encourage submissions with other focuses.

Speakers

TBA

Organizers

Sophie Fellenz
Sophie Fellenz
RPTU Kaiserslautern-Landau
Fabian Jirasek
Fabian Jirasek
RPTU Kaiserslautern-Landau
Pascal Friederich
Pascal Friederich
Karlsruhe Institute of Technology
Stephan Mandt
Stephan Mandt
University of California, Irvine
Alexander Mitsos
Alexander Mitsos
RWTH, Aachen
Maja Rudolph
Maja Rudolph
Data Science Institute at UW-Madison
Hans Hasse
Hans Hasse
RPTU Kaiserslautern-Landau
Frank Glorius
Frank Glorius
University of Münster

Call for Papers

Chemistry, as one of the basic natural sciences, defines the fundamental concepts for many other fields, like medicine, pharmacy, and biotechnology, whereas chemical engineering deals with the technical realization of these concepts in (industrial) processes. Both fields have, over centuries, acquired an exceptionally deep and broad body of domain knowledge, including fundamental natural laws, sophisticated mechanistic models, and rich human expertise and experience. However, machine learning (ML) is rapidly transforming research in these fields, with the potential to also revolutionize industrial processes in the future. By fostering communication and interdisciplinary research in this area, we hope to enrich both fields and allow a wider community to work on the problems important to chemistry and chemical engineering. Fields of applications range from predicting chemical reactions and thermophysical properties of substances to fault detection and automation in chemical processes and plants.

Chemistry and chemical engineering offer exceptional challenges for developing ML models. Data are often sparse, heterogeneous, correlated, and often have high uncertainties, not to mention that they are frequently unstructured. Nevertheless, there are exceptionally high demands on ML methods in chemistry and chemical engineering. Models must be extremely reliable: if a chemical reaction or plant does not behave as predicted, this can have disastrous consequences for humans and the environment. They must also be interpretable and explainable by humans to be accepted in practical applications. Chemistry and chemical engineering provide a vast body of physical and chemical domain knowledge, ranging from (strict) laws of nature and boundary conditions to (soft) empirical correlations, and human experience. Therefore, chemistry and chemical engineering are the ideal fields for exploring the development of hybrid (ML + domain knowledge) models. To sum up, chemistry and chemical engineering offer a unique set of problems, applications, and datasets that require specialized ML methods, and both domains, chemistry and chemical engineering as well as ML, will benefit greatly from a fruitful interdisciplinary exchange during this workshop.

As the first workshop of this type, we keep its scope broad on purpose: fields of applications might include property prediction, force field development, molecule discovery and design, equation discovery, or automation and anomaly detection in chemical processes. However, we explicitly encourage submissions with other focuses.

ML methods of interest include but are not restricted to:

Application fields include but are not restricted to:

Submission Instructions:

We accept both full papers of new original ideas and extended abstracts of already published work that is of interested to the wider ML community. Papers that are accompanied by newly published datasets are also encouraged. Papers can either introduce new ML methods or new ways of applying existing ML methods to new problems (application papers). Full papers should follow the Springer format of regular ECML submissions and be no longer than 16 pages (including references), whereas extended abstracts should be at most 4 pages long. Extended abstracts should include a link to the original publication. The Workshops and Tutorials will be included in a joint Post-Workshop proceeding published by Springer Communications in Computer and Information Science, in 1-2 volumes, organised by focused scope and possibly indexed by WOS. Papers authors will have the faculty to opt-in or opt-out. All submitted papers should be targetted at a general Machine Learning audience and be understandable without prior knowledge in Chemistry or Chemical Engineering. Submitted papers should be anonymized without including any personal or institution information. We will ensure a double-blind peer review process, where papers are not evaluated by someone within the same organization. We encourage high-quality submissions, which should be compelling and high-impact relevance to Machine Learning for Chemistry or Chemical Engineering. There will be a best paper award with support by the Carl Zeiss Foundation. Additionally, excellent papers in the Chemical Engineering domain will be recommended for inclusion in a special issue of the Computers and Chemical Engineering journal. The link to the submission site will be added soon.

Important Dates are:

Kontakt:

info@ml4cce-ecml.com