| Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives AM Schweidtmann, AD Clayton, N Holmes, E Bradford, RA Bourne, ... Chemical Engineering Journal 352, 277-282, 2018 | 322 | 2018 |
| Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm E Bradford, AM Schweidtmann, A Lapkin Journal of Global Optimization 71 (2), 407-438, 2018 | 271 | 2018 |
| Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm E Bradford, AM Schweidtmann, A Lapkin Journal of Global Optimization 71 (2), 439-440, 2018 | 271* | 2018 |
| Deterministic global optimization with artificial neural networks embedded AM Schweidtmann, A Mitsos Journal of Optimization Theory and Applications 180 (3), 925-948, 2019 | 208 | 2019 |
| Machine learning in chemical engineering: A perspective AM Schweidtmann, E Esche, A Fischer, M Kloft, JU Repke, S Sager, ... Chemie Ingenieur Technik 93 (12), 2029-2039, 2021 | 140 | 2021 |
| Automated self-optimisation of multi-step reaction and separation processes using machine learning AD Clayton, AM Schweidtmann, G Clemens, JA Manson, CJ Taylor, ... Chemical Engineering Journal 384, 123340, 2020 | 131 | 2020 |
| Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis Y Amar, AM Schweidtmann, P Deutsch, L Cao, A Lapkin Chemical science 10 (27), 6697-6706, 2019 | 121 | 2019 |
| Graph neural networks for prediction of fuel ignition quality AM Schweidtmann, JG Rittig, A Konig, M Grohe, A Mitsos, M Dahmen Energy & fuels 34 (9), 11395-11407, 2020 | 119 | 2020 |
| Model-based bidding strategies on the primary balancing market for energy-intense processes P Schäfer, HG Westerholt, AM Schweidtmann, S Ilieva, A Mitsos Computers & Chemical Engineering 120, 4-14, 2019 | 69 | 2019 |
| Rational design of ion separation membranes D Rall, D Menne, AM Schweidtmann, J Kamp, L von Kolzenberg, A Mitsos, ... Journal of Membrane Science 569, 209-219, 2019 | 68 | 2019 |
| Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks AM Schweidtmann, WR Huster, JT Lüthje, A Mitsos Computers & Chemical Engineering 121, 67-74, 2019 | 67 | 2019 |
| Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes E Bradford, AM Schweidtmann, D Zhang, K Jing, EA del Rio-Chanona Computers & Chemical Engineering 118, 143-158, 2018 | 67 | 2018 |
| Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning D Rall, AM Schweidtmann, M Kruse, E Evdochenko, A Mitsos, M Wessling Journal of Membrane Science 608, 118208, 2020 | 57 | 2020 |
| Obey validity limits of data-driven models through topological data analysis and one-class classification AM Schweidtmann, JM Weber, C Wende, L Netze, A Mitsos Optimization and engineering 23 (2), 855-876, 2022 | 49 | 2022 |
| Deterministic global optimization with Gaussian processes embedded AM Schweidtmann, D Bongartz, D Grothe, T Kerkenhoff, X Lin, J Najman, ... Mathematical Programming Computation 13 (3), 553-581, 2021 | 49 | 2021 |
| Working fluid selection for organic rankine cycles via deterministic global optimization of design and operation WR Huster, AM Schweidtmann, A Mitsos Optimization and Engineering 21 (2), 517-536, 2020 | 45 | 2020 |
| A Multiobjective Optimization Including Results of Life Cycle Assessment in Developing Biorenewables‐Based Processes D Helmdach, P Yaseneva, PK Heer, AM Schweidtmann, AA Lapkin ChemSusChem 10 (18), 3632-3643, 2017 | 44 | 2017 |
| Simultaneous rational design of ion separation membranes and processes D Rall, AM Schweidtmann, BM Aumeier, J Kamp, J Karwe, K Ostendorf, ... Journal of Membrane Science 600, 117860, 2020 | 43 | 2020 |
| Techno-economic Optimization of a Green-Field Post-Combustion CO2 Capture Process Using Superstructure and Rate-Based Models U Lee, J Burre, A Caspari, J Kleinekorte, AM Schweidtmann, A Mitsos Industrial & Engineering Chemistry Research 55 (46), 12014-12026, 2016 | 39 | 2016 |
| Learning from flowsheets: A generative transformer model for autocompletion of flowsheets G Vogel, LS Balhorn, AM Schweidtmann Computers & Chemical Engineering 171, 108162, 2023 | 35 | 2023 |
Alexander MitsosAVT Systemverfahrenstechnik, RWTH Aachen University and Energy Systems Engineering IEK-10在 alum.mit.edu 的电子邮件经过验证
