Jan G. Rittig

According to our database, Jan G. Rittig authored at least 23 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

A Systematic Evaluation of Molecular Mixture Behavior Prediction.

Roel J. Leenhouts, Nathan K. Morgan, William H. Green Jr., Jan G. Rittig, Florence H. Vermeire

CoRR, May, 2026

Tabular foundation models for in-context prediction of molecular properties.

Karim Ben Hicham, Jan G. Rittig, Martin Grohe, Alexander Mitsos

CoRR, April, 2026

Differentiable Thermodynamic Phase-Equilibria for Machine Learning.

Karim Ben Hicham, Moreno Ascani, Jan G. Rittig, Alexander Mitsos

CoRR, March, 2026

Clapeyron neural networks for single-species vapor-liquid equilibria.

Jan Pavsek, Alexander Mitsos, Elvis J. Sim, Jan G. Rittig

Comput. Chem. Eng., 2026

Predicting the equivalent alkane carbon number of oils using graph neural networks and quantum mechanical descriptors.

Christoforos Brozos, Sandip Bhattacharya, Jan G. Rittig, Elie Akanny, Philipp Eiden, Alexander Mitsos

Comput. Chem. Eng., 2026

2025

Multi-fidelity graph neural networks for predicting toluene/water partition coefficients.

Thomas Nevolianis, Jan G. Rittig, Alexander Mitsos, Kai Leonhard

J. Cheminformatics, December, 2025

Accelerating Scientific Discovery with Autonomous Goal-evolving Agents.

Yuanqi Du, Botao Yu, Tianyu Liu, Tony Shen, Junwu Chen, Jan G. Rittig, Kunyang Sun, Yikun Zhang, Zhangde Song, Bo Zhou, Cassandra Masschelein, Yingze Wang, Haorui Wang, Haojun Jia, Chao Zhang, Hongyu Zhao, Martin Ester, Teresa Head-Gordon, Carla P. Gomes, Huan Sun, Chenru Duan, Philippe Schwaller, Wengong Jin

CoRR, December, 2025

DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks.

Jan Pavsek, Alexander Mitsos, Manuel Dahmen, Tai Xuan Tan, Jan G. Rittig

CoRR, September, 2025

Molecular Machine Learning in Chemical Process Design.

Jan G. Rittig, Manuel Dahmen, Martin Grohe, Philippe Schwaller, Alexander Mitsos

CoRR, August, 2025

Federated Learning from Molecules to Processes: A Perspective.

Jan G. Rittig, Clemens Kortmann

CoRR, June, 2025

Graph machine learning for molecular property prediction and design.

Jan G. Rittig

PhD thesis, 2025

Exploring data augmentation: Multi-task methods for molecular property prediction.

Muhammad bin Javaid, Timo Gervens, Alexander Mitsos, Martin Grohe, Jan G. Rittig

Comput. Chem. Eng., 2025

Predicting the temperature-dependent CMC of surfactant mixtures with graph neural networks.

Christoforos Brozos, Jan G. Rittig, Elie Akanny, Sandip Bhattacharya, Christina Kohlmann, Alexander Mitsos

Comput. Chem. Eng., 2025

2024

GraphXForm: Graph transformer for computer-aided molecular design with application to extraction.

Jonathan Pirnay, Jan G. Rittig, Alexander B. Wolf, Martin Grohe, Jakob Burger, Alexander Mitsos, Dominik G. Grimm

CoRR, 2024

Thermodynamics-Consistent Graph Neural Networks.

Jan G. Rittig, Alexander Mitsos

CoRR, 2024

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks.

Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, Elie Akanny, Christina Kohlmann, Alexander Mitsos

CoRR, 2024

Graph Neural Networks for Surfactant Multi-Property Prediction.

Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, Elie Akanny, Christina Kohlmann, Alexander Mitsos

CoRR, 2024

2023

Physical pooling functions in graph neural networks for molecular property prediction.

Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai Leonhard, Alexander Mitsos

Comput. Chem. Eng., April, 2023

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids.

Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, Manuel Dahmen, Alexander Mitsos

Comput. Chem. Eng., March, 2023

Software for "Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction".

Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai Leonhard, Alexander Mitsos

Dataset, January, 2023

Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction.

Jan G. Rittig, Kobi C. Felton, Alexei A. Lapkin, Alexander Mitsos

CoRR, 2023

2022

Graph neural networks for the prediction of molecular structure-property relationships.

Jan G. Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, Artur M. Schweidtmann

CoRR, 2022

Graph Machine Learning for Design of High-Octane Fuels.

Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann, Stefanie Winkler, Jana M. Weber, Philipp Morsch, K. Alexander Heufer, Martin Grohe, Alexander Mitsos, Manuel Dahmen

CoRR, 2022