| Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization G Trezza, L Bergamasco, M Fasano, E Chiavazzo npj Computational Materials 8 (1), 1-14, 2022 | 23 | 2022 |
| Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms SA Bonke, G Trezza, L Bergamasco, H Song, S Rodríguez-Jiménez, ... Journal of the American Chemical Society 146 (22), 15648-15658, 2024 | 12 | 2024 |
| Leveraging composition-based energy material descriptors for machine learning models G Trezza, E Chiavazzo Materials Today Communications, 106579, 2023 | 8* | 2023 |
| Learning Effective Good Variables from Physical Data G Barletta, G Trezza, E Chiavazzo Machine Learning and Knowledge Extraction 6 (3), 1597-1618, 2024 | 3 | 2024 |
| Classification-Based Detection and Quantification of Cross-Domain Data Bias in Materials Discovery G Trezza, E Chiavazzo Journal of Chemical Information and Modeling, 2024 | 1 | 2024 |
| Energy-GNoME: A Living Database of Selected Materials for Energy Applications P De Angelis, G Trezza, G Barletta, P Asinari, E Chiavazzo arXiv preprint arXiv:2411.10125, 2024 | | 2024 |
| Artificial Intelligence based screening of materials for energy storage applications G Trezza Politecnico di Torino, 2024 | | 2024 |
| Optimizing MOF properties for seasonal heat storage: a machine learning approach G Trezza, L Bergamasco, M Fasano, E Chiavazzo Journal of Physics: Conference Series 2766 (1), 012219, 2024 | | 2024 |
Eliodoro ChiavazzoPolitecnico di TorinoΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα polito.it