| Graph neural networks for the prediction of infinite dilution activity coefficients EIS Medina, S Linke, M Stoll, K Sundmacher Digital Discovery 1 (3), 216-225, 2022 | 45 | 2022 |
| Hybrid semi‐parametric modeling in separation processes: a review K McBride, EI Sanchez Medina, K Sundmacher Chemie Ingenieur Technik 92 (7), 842-855, 2020 | 40 | 2020 |
| Impacts of antiscalants on the formation of calcium solids: Implication on scaling potential of desalination concentrate T Jain, E Sanchez, E Owens-Bennett, R Trussell, S Walker, H Liu Environmental Science: Water Research & Technology 5 (7), 1285-1294, 2019 | 37 | 2019 |
| Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution EIS Medina, S Linke, M Stoll, K Sundmacher Digital Discovery 2 (3), 781-798, 2023 | 16 | 2023 |
| Understanding the dynamic behaviour of semicontinuous distillation PB Madabhushi, EIS Medina, TA Adams II Computer Aided Chemical Engineering 43, 845-850, 2018 | 10 | 2018 |
| Gibbs–helmholtz graph neural network for the prediction of activity coefficients of polymer solutions at infinite dilution EI Sanchez Medina, S Kunchapu, K Sundmacher The Journal of Physical Chemistry A 127 (46), 9863-9873, 2023 | 5 | 2023 |
| Computer Aided Chemical Engineering EI Sanchez Medina, K Sundmacher Elsevier 52, 2037-2042, 2023 | 5 | 2023 |
| Machine learning-supported solvent design for lignin-first biorefineries and lignin upgrading L König-Mattern, EIS Medina, AO Komarova, S Linke, ... Chemical Engineering Journal 495, 153524, 2024 | 2 | 2024 |
| Solvent pre-selection for extractive distillation using Gibbs-Helmholtz Graph Neural Networks EIS Medina, K Sundmacher Computer Aided Chemical Engineering 52, 2037-2042, 2023 | 2 | 2023 |
| Prediction of bioconcentration factors (bcf) using graph neural networks EIS Medina, S Linke, K Sundmacher Computer Aided Chemical Engineering 50, 991-997, 2021 | 2 | 2021 |
| Acyclic modular flowsheet optimization using multiple trust regions and Gaussian process regression EIS Medina, DR Vallejo, B Chachuat, K Sundmacher, P Petsagkourakis, ... Computer Aided Chemical Engineering 50, 1117-1123, 2021 | 2 | 2021 |
| Graph neural networks for CO2 solubility predictions in Deep Eutectic Solvents GH Morales, EIS Medina, A Jiménez-Gutiérrez, VM Zavala Computers & Chemical Engineering, 108750, 2024 | 1 | 2024 |
| A symbolic regression based methodology for the construction of interpretable and predictive thermodynamic models S Kay, EIS Medina, K Sundmacher, D Zhang Computer aided chemical engineering 53, 2701-2706, 2024 | 1 | 2024 |
| Multi-Objective Bayesian optimization of process flowsheets using trust regions and quality set metrics. EI Sanchez Medina, DF Rodriguez-Vallejo, EA del Rio-Chanona, ... 2021 AIChE Annual Meeting, 2021 | 1 | 2021 |
| Hybrid Graph Neural Networks for the prediction of activity coefficients in separation processes EI Sanchez Medina Otto-von-Guericke-Universität Magdeburg, 2024 | | 2024 |
| Graph Neural Networks for CO2 Solubility Predictions in Deep Eutectic Solvents EIS Medina, GH Morales, A Jimenez-Gutierrez, VM Zavala | | 2024 |
| A Comparison of the UNIFAC Model vs. Graph Neural Network-based Models for the Prediction of Binary Vapor-Liquid Equilibria EI Sanchez Medina, AJ Minor, K Sundmacher 33rd European Symposium on Applied Thermodynamics 2024, 2024 | | 2024 |
| PSEvolve: A graph-based solvent design framework L König-Mattern, EI Sanchez Medina, AO Komarova, S Linke, ... ESCAPE34, 2024 | | 2024 |
| Rapid phosphine ligand discovery in homogeneous catalysis: Bayesian optimization approach for low-throughput experimentation C Griehl, EI Sanchez Medina, K Sundmacher CHISA 2024, 2024 | | 2024 |
| BOHO CAT-Bayesian Optimization for Low Throughput Ligand Selection in Homogeneous Catalysis C Griehl, EI Sanchez Medina, K Sundmacher Cambridge ELLIS Unit Summer School 2024, 2024 | | 2024 |
Kai SundmacherProfessor of Process Systems Engineering, Max Planck Institute for Dynamics of Complex TechnicalVerified email at mpi-magdeburg.mpg.de