Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids

no code implementations • 23 Jun 2022 • Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, Manuel Dahmen, Alexander Mitsos

We train the GNN on a database including more than 40, 000 AC values and compare it to a state-of-the-art MCM.

Matrix Completion

Graph Machine Learning for Design of High-Octane Fuels

no code implementations • 1 Jun 2022 • Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann, Stefanie Winkler, Jana M. Weber, Philipp Morsch, K. Alexander Heufer, Martin Grohe, Alexander Mitsos, Manuel Dahmen

We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space.

Global Optimization of Gaussian processes

no code implementations • 21 May 2020 • Artur M. Schweidtmann, Dominik Bongartz, Daniel Grothe, Tim Kerkenhoff, Xiaopeng Lin, Jaromil Najman, Alexander Mitsos

Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems.

Gaussian Processes