48 citations · 124 across the 15 of their papers we have counts for
15 papers
Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection
Jennifer Werner, Justus Arweiler, Indra Jungjohann +4
Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated training datasets that are rare…
CHAOS -- A Consistent Large-scale Database for Sigma-Profiles and Other Molecular Descriptors
Dominik Gond, Justus Arweiler, Thomas Specht +2
Sigma-profiles obtained from quantum-chemical calculations are key molecular descriptors for solvent selection, thermodynamic modeling, and data-driven molecular design. However, e…
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner, Arjun Nair, Billy Joe Franks +24
Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection m…
DiffStyleTS: Diffusion Model for Style Transfer in Time Series
Mayank Nagda, Phil Ostheimer, Justus Arweiler +13
Style transfer combines the content of one signal with the style of another. It supports applications such as data augmentation and scenario simulation, helping machine learning mo…
Thermodynamically consistent machine learning model for excess Gibbs energy
Marco Hoffmann, Thomas Specht, Quirin Göttl +4
The excess Gibbs energy plays a central role in chemical engineering and chemistry, providing a basis for modeling thermodynamic properties of liquid mixtures. Predicting the exces…
Superstudent intelligence in thermodynamics
Rebecca Loubet, Pascal Zittlau, Marco Hoffmann +6
In this short note, we report and analyze a striking event: OpenAI's large language model o3 has outwitted all students in a university exam on thermodynamics. The thermodynamics e…
MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning
Marco Hoffmann, Thomas Specht, Nicolas Hayer +2
Machine learning (ML) enables the development of powerful methods for predicting thermophysical properties with unprecedented scope and accuracy. However, technical barriers like c…
Using Large Language Models for Solving Thermodynamic Problems
Rebecca Loubet, Pascal Zittlau, Luisa Vollmer +5
Large Language Models (LLMs) have made significant progress in reasoning, demonstrating their capability to generate human-like responses. This study analyzes the problem-solving c…
GRAPPA -- A Hybrid Graph Neural Network for Predicting Pure Component Vapor Pressures
Marco Hoffmann, Hans Hasse, Fabian Jirasek
Although the pure component vapor pressure is one of the most important properties for designing chemical processes, no broadly applicable, sufficiently accurate, and open-source p…
Modified UNIFAC 2.0 -- A Group-Contribution Method Completed with Machine Learning
Nicolas Hayer, Hans Hasse, Fabian Jirasek
Predicting thermodynamic properties of mixtures is a cornerstone of chemical engineering, yet conventional group-contribution (GC) methods like modified UNIFAC (Dortmund) remain li…
Prediction of Activity Coefficients by Similarity-Based Imputation using Quantum-Chemical Descriptors
Nicolas Hayer, Thomas Specht, Justus Arweiler +3
In this work, we introduce a novel approach for predicting thermodynamic properties of binary mixtures, which we call the similarity-based method (SBM). The method is based on quan…
Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures
Dominik Gond, Jan-Tobias Sohns, Heike Leitte +2
Predicting the thermodynamic properties of mixtures is crucial for process design and optimization in chemical engineering. Machine learning (ML) methods are gaining increasing att…
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0
Nicolas Hayer, Thorsten Wendel, Stephan Mandt +2
Accurate prediction of thermodynamic properties is pivotal in chemical engineering for optimizing process efficiency and sustainability. Physical group-contribution (GC) methods ar…
HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction
Thomas Specht, Mayank Nagda, Sophie Fellenz +3
We present the first hard-constraint neural network for predicting activity coefficients (HANNA), a thermodynamic mixture property that is the basis for many applications in scienc…
KnowTD-An Actionable Knowledge Representation System for Thermodynamics
Luisa Vollmer, Sophie Fellenz, Fabian Jirasek +2
We demonstrate that thermodynamic knowledge acquired by humans can be transferred to computers so that the machine can use it to solve thermodynamic problems and produce explainabl…