Artur M. Schweidtmann

Delft University of Technology

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Alexander Mitsos

Jülich Aachen Research Alliance

Alexei A. Lapkin

University of Cambridge

Lukas Schulze Balhorn

Delft University of Technology

Manuel Dahmen

Forschungszentrum Jülich

Jan G. Rittig

RWTH Aachen University

Jana M. Weber

Delft University of Technology

Dominik Bongartz

KU Leuven

Wolfgang R. Huster

RWTH Aachen University

Pascal M. Schäfer

RWTH Aachen University

Eric Bradford

ETH Zurich

Cooperative Institutions

Software (Spain)

RWTH Aachen University

TU Dortmund University

Delft University of Technology

University of Cambridge

FH Aachen

Technical University of Munich

E Ink (South Korea)

The Cambridge Centre for Advanced Research and Education in Singapore

Ruhr University Bochum

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Physical pooling functions in graph neural networks for molecular property prediction

Computers & Chemical Engineering (2023)

Artur M. Schweidtmann Jan G. Rittig Jana M. Weber Martin Grohe Manuel Dahmen

Pooling

Molecular graph

10.1016/j.compchemeng.2023.108202

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Citations (27)

Efficient Bayesian Uncertainty Estimation for nnU-Net

Lecture notes in computer science (2022)

Yidong Zhao Changchun Yang Artur M. Schweidtmann Qian Tao

Baseline (sea)

10.1007/978-3-031-16452-1_51

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Citations (12)

Optimization with Trained Machine Learning Models Embedded

Encyclopedia of Optimization (2022)

Artur M. Schweidtmann Dominik Bongartz Alexander Mitsos

Abstract Text

10.1007/978-3-030-54621-2_735-1

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Citations (1)

Graph Neural Networks for Prediction of Fuel Ignition Quality

Artur M. Schweidtmann Jan G. Rittig Andrea König Martin Grohe Alexander Mitsos

Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.

Molecular graph

Cetane number

10.26434/chemrxiv.12280325.v1

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Citations (1)

Hybrid Mechanistic Data-Driven Modeling for the Deterministic Global Optimization of a Transcritical Organic Rankine Cycle : ESCAPE 30 : European Symposium on Computer Aided Process Engineering ; 31. Aug. - 2. Sep. 2020, Milan Italy

Wolfgang R. Huster Artur M. Schweidtmann Alexander Mitsos

Degree Rankine

Organic Rankine Cycle

10.18154/rwth-2020-10109

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Advanced Process Control for Optimal Operation of an Air Separation Unit under Fast-Changing Market Dynamics

Luise Bering Adel Mhamdi Artur M. Schweidtmann Adrian Caspari Pascal M. Schäfer

Separation (statistics)

Process dynamics

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Toward automatic generation of control structures for process flow diagrams with large language models

AIChE Journal (2023)

Edwin Hirtreiter Lukas Schulze Balhorn Artur M. Schweidtmann

Abstract Developing Piping and Instrumentation Diagrams (P&IDs) is a crucial step during process development. We propose a data‐driven method for the prediction of control structures. Our methodology is inspired by end‐to‐end transformer‐based human language translation models. We cast the control structure prediction as a translation task where Process Flow Diagrams (PFDs) without control structures are translated to PFDs with control structures. We represent the topology of PFDs as strings using the SFILES 2.0 notation. We pretrain our model using generated PFDs to learn the grammatical structure. Thereafter, the model is fine‐tuned leveraging transfer learning on real PFDs. The model achieved a top‐5 accuracy of 74.8% on 10,000 generated PFDs and 89.2% on 100,000 generated PFDs. These promising results show great potential for AI‐assisted process engineering. The tests on a dataset of 312 real PFDs indicate the need for a larger PFD dataset for industry applications and hybrid artificial intelligence solutions.

10.1002/aic.18259

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Citations (12)

Artificial Neural Networks Design Layer-by-Layer Nanofiltration Membranes

Deniz Rall Lars von Kolzenberg Johannes Kamp Artur M. Schweidtmann Daniel Menne

Nanofiltration

Source

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Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices

Computers & Chemical Engineering (2019)

Pascal M. Schäfer Artur M. Schweidtmann Philipp H.A. Lenz Hannah M.C. Markgraf Alexander Mitsos

Solver

10.1016/j.compchemeng.2019.106598

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Citations (15)

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb

Frontiers in Bioinformatics (2021)

Ulf W. Liebal Sebastian Köbbing Linus Netze Artur M. Schweidtmann Alexander Mitsos

Metabolic engineering relies on modifying gene expression to regulate protein concentrations and reaction activities. The gene expression is controlled by the promoter sequence, and sequence libraries are used to scan expression activities and to identify correlations between sequence and activity. We introduce a computational workflow called Exp2Ipynb to analyze promoter libraries maximizing information retrieval and promoter design with desired activity. We applied Exp2Ipynb to seven prokaryotic expression libraries to identify optimal experimental design principles. The workflow is open source, available as Jupyter Notebooks and covers the steps to 1) generate a statistical overview to sequence and activity, 2) train machine-learning algorithms, such as random forest, gradient boosting trees and support vector machines, for prediction and extraction of feature importance, 3) evaluate the performance of the estimator, and 4) to design new sequences with a desired activity using numerical optimization. The workflow can perform regression or classification on multiple promoter libraries, across species or reporter proteins. The most accurate predictions in the sample libraries were achieved when the promoters in the library were recognized by a single sigma factor and a unique reporter system. The prediction confidence mostly depends on sample size and sequence diversity, and we present a relationship to estimate their respective effects. The workflow can be adapted to process sequence libraries from other expression-related problems and increase insight to the growing application of high-throughput experiments, providing support for efficient strain engineering.

Feature Engineering

Boosting

10.3389/fbinf.2021.747428

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Citations (8)