| Chemical shifts in molecular solids by machine learning FM Paruzzo, A Hofstetter, F Musil, S De, M Ceriotti, L Emsley Nature communications 9 (1), 4501, 2018 | 228 | 2018 |
| A Bayesian approach to NMR crystal structure determination EA Engel, A Anelli, A Hofstetter, F Paruzzo, L Emsley, M Ceriotti Physical Chemistry Chemical Physics 21 (42), 23385-23400, 2019 | 52 | 2019 |
| Structure elucidation of a complex CO 2-based organic framework material by NMR crystallography J Leclaire, G Poisson, F Ziarelli, G Pepe, F Fotiadu, FM Paruzzo, ... Chemical science 7 (7), 4379-4390, 2016 | 52 | 2016 |
| Structure determination of an amorphous drug through large-scale NMR predictions M Cordova, M Balodis, A Hofstetter, F Paruzzo, SO Nilsson Lill, ... Nature Communications 12 (1), 2964, 2021 | 51 | 2021 |
| Rapid structure determination of molecular solids using chemical shifts directed by unambiguous prior constraints A Hofstetter, M Balodis, FM Paruzzo, CM Widdifield, G Stevanato, ... Journal of the American Chemical Society 141 (42), 16624-16634, 2019 | 45 | 2019 |
| Deconvolution of 1D NMR spectra: A deep learning-based approach N Schmid, S Bruderer, F Paruzzo, G Fischetti, G Toscano, D Graf, M Fey, ... Journal of Magnetic Resonance 347, 107357, 2023 | 32 | 2023 |
| High-resolution 1H NMR of powdered solids by homonuclear dipolar decoupling FM Paruzzo, L Emsley Journal of Magnetic Resonance 309, 106598, 2019 | 31 | 2019 |
| Homonuclear Decoupling in 1H NMR of Solids by Remote Correlation P Moutzouri, FM Paruzzo, B Simões de Almeida, G Stevanato, L Emsley Angewandte Chemie 132 (15), 6294-6297, 2020 | 25 | 2020 |
| A machine learning model of chemical shifts for chemically and structurally diverse molecular solids M Cordova, EA Engel, A Stefaniuk, F Paruzzo, A Hofstetter, M Ceriotti, ... The Journal of Physical Chemistry C 126 (39), 16710-16720, 2022 | 24 | 2022 |
| Line narrowing in 1H NMR of powdered organic solids with TOP-CT-MAS experiments at ultra-fast MAS FM Paruzzo, BJ Walder, L Emsley Journal Of Magnetic Resonance 305, 131-137, 2019 | 18 | 2019 |
| Atomic‐Scale Description of Interfaces between Antigen and Aluminum‐Based Adjuvants Used in Vaccines by Dynamic Nuclear Polarization (DNP) Enhanced NMR Spectroscopy J Viger‐Gravel, FM Paruzzo, C Cazaux, R Jabbour, A Leleu, F Canini, ... Chemistry–A European Journal 26 (41), 8976-8982, 2020 | 16 | 2020 |
| Refocused linewidths less than 10 Hz in 1H solid-state NMR FM Paruzzo, G Stevanato, ME Halse, J Schlagnitweit, D Mammoli, ... Journal of Magnetic Resonance 293, 41-46, 2018 | 6 | 2018 |
| Deep learning-based phase and baseline correction of 1D 1H NMR Spectra S Bruderer, F Paruzzo, C Bolliger Public Bruker White Paper, 2021 | 5 | 2021 |
| Measurement of Proton Spin Diffusivity in Hydrated Cementitious Solids BJ Walder, NA Prisco, FM Paruzzo, JR Yarava, BF Chmelka, L Emsley The journal of physical chemistry letters 10 (17), 5064-5069, 2019 | 4 | 2019 |
| Automatic signal region detection in 1H NMR spectra using deep learning F Paruzzo, S Bruderer, Y Janjar, B Heitmann, C Bolliger Bruker Whitepaper, https://www. bruker. com/en/products-and-solutions/mr …, 2020 | 3 | 2020 |
| New Approaches to NMR Crystallography FM Paruzzo EPFL, 2019 | 1 | 2019 |
| Atomic-level structure of the amorphous drug Atuliflapon by NMR crystallography J Holmes, D Torodii, M Balodis, M Cordova, A Hofstetter, F Paruzzo, ... Faraday Discussions, 2024 | | 2024 |
| A deep ensemble learning method for automatic classification of multiplets in 1D NMR spectra G Fischetti, N Schmid, S Bruderer, F Paruzzo, G Toscano, D Graf, M Fey, ... European Conference on Magnetic Resonance (EUROMAR), Utrecht, The …, 2022 | | 2022 |
| NMR MEETS MACHINE LEARNING: CHEMICAL SHIFT PREDICTIONS IN SOLIDS IN LESS THAN A MINUTE FM Paruzzo, A Hofstetter, F Musil, S De, M Ceriotti, L Emsley XLVII National Congress on Magnetic Resonance, 37, 0 | | |
| LRM M Balodis, P Berruyer, A Bertarello, S Björgvinsdóttir, B Busi, M Cordova, ... | | |
