| Error estimates for density-functional theory predictions of surface energy and work function S De Waele, K Lejaeghere, M Sluydts, S Cottenier Physical Review B 94 (23), 235418, 2016 | 151 | 2016 |
| Controlling the size of hot injection made nanocrystals by manipulating the diffusion coefficient of the solute K De Nolf, RK Capek, S Abe, M Sluydts, Y Jang, JC Martins, S Cottenier, ... Journal of the American Chemical Society 137 (7), 2495-2505, 2015 | 80 | 2015 |
| Ligand Addition Energies and the Stoichiometry of Colloidal Nanocrystals M Sluydts, K De Nolf, V Van Speybroeck, S Cottenier, Z Hens ACS nano 10 (1), 1462-1474, 2016 | 38 | 2016 |
| Mechanistic insights in seeded growth synthesis of colloidal core/shell quantum dots I Nakonechnyi, M Sluydts, Y Justo, J Jasieniak, Z Hens Chemistry of Materials 29 (11), 4719-4727, 2017 | 35 | 2017 |
| How to verify the precision of density-functional-theory implementations via reproducible and universal workflows E Bosoni, L Beal, M Bercx, P Blaha, S Blügel, J Bröder, M Callsen, ... Nature Reviews Physics 6 (1), 45-58, 2024 | 33 | 2024 |
| Race against the Machine: can deep learning recognize microstructures as well as the trained human eye? M Larmuseau, M Sluydts, K Theuwissen, L Duprez, T Dhaene, S Cottenier Scripta Materialia 193, 33-37, 2021 | 33 | 2021 |
| Compact representations of microstructure images using triplet networks M Larmuseau, M Sluydts, K Theuwissen, L Duprez, T Dhaene, S Cottenier npj Computational Materials 6 (1), 156, 2020 | 25 | 2020 |
| High-throughput screening of extrinsic point defect properties in Si and Ge: database and applications M Sluydts, M Pieters, J Vanhellemont, V Van Speybroeck, S Cottenier Chemistry of Materials 29 (3), 975-984, 2017 | 15 | 2017 |
| DFT insights into surface properties of anti-perovskite 3D topological crystalline insulators: A case study of (001) surfaces of Ca3SnO M Bilal, SM Alay-e-Abbas, M Sluydts, J Batool, A Laref, G Abbas, N Amin Physics Letters A 408, 127469, 2021 | 5 | 2021 |
| The road to accuracy: machine-learning-accelerated silicon ab initio simulations M Sluydts, M Larmuseau, J Lauwaert, S Cottenier arXiv preprint arXiv:1903.10216, 2019 | 2 | 2019 |
| The devil in the details: lessons from Li 6 PS 5 X for robust high-throughput workflows AI Bhatti, S Kumar, C Jaeken, M Sluydts, DEP Vanpoucke, S Cottenier Journal of Materials Chemistry A 13 (1), 526-539, 2025 | | 2025 |
| Queue Manager: high-throughput density-functional theory screening software for materials discovery and design M Sluydts Ghent University, 2016 | | 2016 |
| Controlling the Size of Hot-Injection Made Nanocrystals by Manipulating the Diffusion Coefficient of the Solute Supporting Information K De Nolf, RK Capek, S Abe, M Sluydts, Y Jang, JC Martins, S Cottenier, ... | | |
| Ligand engineering with colloidal nanocrystals M Sluydts | | |
Stefaan CottenierGhent UniversityEmail verificata su ugent.be