Fabian Jirasek

Juniorprofessor

Technische Universität Kaiserslautern

Germany

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Fabian Jirasek is a Juniorprofessor at Technische Universität Kaiserslautern in Germany. His research focuses on the application of machine learning and matrix completion methods to predict thermodynamic properties and activity coefficients of chemical mixtures. Recent publications include work on hierarchical matrix completion, hybrid graph neural networks, and innovative group-contribution methods aimed at advancing the field of thermodynamics.

Articles (16)

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2025

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2025

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2025

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2021

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2021

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2021

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2020

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2020

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2020

Collaborators (2)

Heike Leitte

Professor

TU Kaiserslautern

GERMANY

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Stephan Mandt

Associate Professor

University of California, Irvine

UNITED STATES

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Fabian Jirasek

Research Interests

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About

Articles (16)

Hierarchical matrix completion for the prediction of properties of binary mixtures

GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures

Similarity-Informed Matrix Completion Method for Predicting Activity Coefficients

Prediction of Temperature-Dependent Henry’s Law Constants by Matrix Completion

Advancing thermodynamic group-contribution methods by machine learning: UNIFAC 2.0

Balancing molecular information and empirical data in the prediction of physico-chemical properties

Prediction of activity coefficients by similarity-based imputation using quantum-chemical descriptors

KnowTD─An Actionable Knowledge Representation System for Thermodynamics

Automated nuclear magnetic resonance fingerprinting of mixtures

HANNA: hard-constraint neural network for consistent activity coefficient prediction

Predicting Activity Coefficients at Infinite Dilution for Varying Temperatures by Matrix Completion

Automated Methods for Identification and Quantification of Structural Groups from Nuclear Magnetic Resonance Spectra Using Support Vector Classification

Influence of Salts on the Adsorption of Lysozyme on a Mixed-Mode Resin

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion

Application of NEAT for the simulation of liquid–liquid extraction processes with poorly specified feeds

Hybridizing physical and data-driven prediction methods for physicochemical properties

Collaborators (2)

Heike Leitte

Stephan Mandt

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