Jan G. Rittig

Artificial Intelligence in Medicine (Canada) — Canada

Materials Science · Physical Sciences

14h-index614citations43works4.82yr avg
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Research Topics

Machine Learning in Materials Science(25), Computational Drug Discovery Methods(21), Protein Structure and Dynamics(6), Chemical Thermodynamics and Molecular Structure(5), Enhanced Oil Recovery Techniques(4)

Publications43 total

Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
arXiv (Cornell University)·2025Open Access
Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
ArXiv.org·2025Open Access
Predicting the equivalent alkane carbon number of oils using graph neural networks and quantum mechanical descriptors
Computers & Chemical Engineering·2025Open Access
DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks
arXiv (Cornell University)·2025Open Access
RAMBOAU: Risk-averse Multiobjective Bayesian Optimization with Aleatoric Uncertainty for Nanomaterial Synthesis
2025Open Access
Molecular Machine Learning in Chemical Process Design
arXiv (Cornell University)·2025Open Access
Multi-fidelity graph neural networks for predicting toluene/water partition coefficients
Journal of Cheminformatics·2025Open Access
Exploring data augmentation: Multi-task methods for molecular property prediction
Computers & Chemical Engineering·2025Open Access
Predicting the temperature-dependent CMC of surfactant mixtures with graph neural networks
Computers & Chemical Engineering·2025· 5 citedOpen Access
GraphXForm: graph transformer for computer-aided molecular design
Digital Discovery·2025· 3 citedOpen Access
Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks
arXiv (Cornell University)·2024Open Access
GraphXForm: Graph transformer for computer-aided molecular design
arXiv (Cornell University)·2024· 1 citedOpen Access
Multi-fidelity graph neural networks for predicting toluene/water partition coefficients
2024· 5 citedOpen Access
Fuel Ignition Delay Maps for Molecularly Controlled Combustion
Energy & Fuels·2024· 2 cited
Thermodynamics-Consistent Graph Neural Networks
arXiv (Cornell University)·2024· 1 citedOpen Access
Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
Journal of Chemical Theory and Computation·2024· 16 citedOpen Access
ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction
Chemical Engineering Journal·2024· 18 citedOpen Access
Graph neural networks for surfactant multi-property prediction
Colloids and Surfaces A Physicochemical and Engineering Aspects·2024· 20 citedOpen Access
Graph Neural Networks for Surfactant Multi-Property Prediction
arXiv (Cornell University)·2024· 1 citedOpen Access
Thermodynamics-consistent graph neural networks
Chemical Science·2024· 25 citedOpen Access
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Frequent Co-authors

Alexander Mitsos(36), Manuel Dahmen(18), Artur M. Schweidtmann(15), Martin Grohe(12), Alexei A. Lapkin(9), Kobi Felton(8), Kai Leonhard(8), Andrea König(6), Christoforos Brozos(6), Sandip Bhattacharya(6), Elie Akanny(6), Christina Kohlmann(5), Jana M. Weber(4), Karim Ben Hicham(3), Martin Ritzert(2), Stefanie Winkler(2), Philipp Morsch(2), Qinghe Gao(2), Lorenz Fleitmann(2), Philipp Ackermann(2)