| Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats MR Dobbelaere, PP Plehiers, R Van de Vijver, CV Stevens, ... Engineering, 2021 | 168 | 2021 |
| Machine learning for physicochemical property prediction of complex hydrocarbon mixtures MR Dobbelaere, Y Ureel, FH Vermeire, L Tomme, CV Stevens, ... Industrial & Engineering Chemistry Research 61 (24), 8581-8594, 2022 | 25 | 2022 |
| Speeding up turbulent reactive flow simulation via a deep artificial neural network: A methodology study Y Ouyang, LA Vandewalle, L Chen, PP Plehiers, MR Dobbelaere, ... Chemical Engineering Journal 429, 132442, 2022 | 19 | 2022 |
| Artificial Intelligence for Computer-Aided Synthesis In Flow: Analysis and Selection of Reaction Components PP Plehiers, CW Coley, H Gao, FH Vermeire, MR Dobbelaere, ... Frontiers in Chemical Engineering 2, 5, 2020 | 18 | 2020 |
| Learning Molecular Representations for Thermochemistry Prediction of Cyclic Hydrocarbons and Oxygenates MR Dobbelaere, PP Plehiers, R Van de Vijver, CV Stevens, ... The Journal of Physical Chemistry A, 2021 | 14 | 2021 |
| Active learning-based exploration of the catalytic pyrolysis of plastic waste Y Ureel, MR Dobbelaere, O Akin, RJ Varghese, CG Pernalete, ... Fuel 328, 125340, 2022 | 13 | 2022 |
| Bayesian tuned kinetic Monte Carlo modeling of polystyrene pyrolysis: Unraveling the pathways to its monomer, dimers, and trimers formation O Dogu, A Eschenbacher, RJ Varghese, M Dobbelaere, DR D'hooge, ... Chemical Engineering Journal 455, 140708, 2023 | 10 | 2023 |
| Machine learning in chemical engineering: strengths, weaknesses, opportunities, and threats. Engineering 7, 1201–1211 MR Dobbelaere, PP Plehiers, R Van de Vijver, CV Stevens, ... | 7 | 2021 |
| Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead! Y Ureel, MR Dobbelaere, Y Ouyang, K De Ras, MK Sabbe, GB Marin, ... Engineering, 2023 | 5 | 2023 |
| Bayesian Tuned Kinetic Monte Carlo Modeling of Polystyrene Pyrolysis: unraveling the Pathways to Monomer, Dimers, and Trimers of Polystyrene O Dogu, A Eschenbacher, RJ Varghese, M Dobbelaere, D D'hooge, ... Dimers, and Trimers of Polystyrene, 2022 | 1 | 2022 |
| Rxn-INSIGHT: fast chemical reaction analysis using bond-electron matrices MR Dobbelaere, I Lengyel, CV Stevens, KM Van Geem Journal of Cheminformatics 16 (1), 37, 2024 | | 2024 |
| Using Thermodynamic Principles to Enhance Machine Learning Predictions of Activation Energy L Tomme, M Dobbelaere, F Vermeire, C Stevens, K Van Geem 14th European Congress of Chemical Engineering and 7th European Congress of …, 2023 | | 2023 |
| Reaction Data in Flow Chemistry: a Power or a Weakness? M Dobbelaere, I Lengyel, C Stevens, K Van Geem ACS Fall 2023 (ACS Fall 2023), 2023 | | 2023 |
| Ultrafast Solvent Selection with Geometric Message Passing Neural Networks M Dobbelaere, I Lengyel, D West, C Stevens, K Van Geem ACS Fall 2023 (ACS Fall 2023), 2023 | | 2023 |
| Exploring the Reaction Space of Flow Chemistry M Dobbelaere, L Tomme, I Lengyel, C Stevens, K Van Geem Solvay Workshop" New ways to do chemistry Emerging technologies for …, 2023 | | 2023 |
| Towards Big Data in Flow Chemistry: Popularity Trends and Best Practices M Dobbelaere, L Tomme, I Lengyel, C Stevens, K Van Geem Flow Chemistry Europe 2023 (FCES23), 2023 | | 2023 |
| Exploration of catalytic pyrolysis with active learning Y Ureel, M Dobbelaere, O Akin, R John Varghese, C Pernalete Piña, ... Chemical Research in Flanders: Chemistry Conference for Young Scientists …, 2022 | | 2022 |
| Design of sustainable pharmaceutical processes with artificial intelligence M Dobbelaere, K Van Geem CESPE conference 2022, 2022 | | 2022 |
| On the relevance of molecular machine learning in industrial chemistry M Dobbelaere, C Stevens, K Van Geem Chemical Research in Flanders: Chemistry Conference for Young Scientists …, 2022 | | 2022 |
| 化学工程中机器学习的优势, 限制, 机会和挑战 MR Dobbelaere, PP Plehiers, R Van de Vijver, CV Stevens, ... Engineering, 2021 | | 2021 |
kevin van geemuniversiteit gentVerified email at ugent.be
