| Incompleteness of atomic structure representations SN Pozdnyakov, MJ Willatt, AP Bartók, C Ortner, G Csányi, M Ceriotti Physical Review Letters 125 (16), 166001, 2020 | 206 | 2020 |
| Atom-density representations for machine learning MJ Willatt, F Musil, M Ceriotti The Journal of chemical physics 150 (15), 2019 | 184 | 2019 |
| Fast and accurate uncertainty estimation in chemical machine learning F Musil, MJ Willatt, MA Langovoy, M Ceriotti Journal of chemical theory and computation 15 (2), 906-915, 2019 | 166 | 2019 |
| Boltzmann-conserving classical dynamics in quantum time-correlation functions:“Matsubara dynamics” TJH Hele, MJ Willatt, A Muolo, SC Althorpe The Journal of Chemical Physics 142 (13), 2015 | 140 | 2015 |
| Communication: Relation of centroid molecular dynamics and ring-polymer molecular dynamics to exact quantum dynamics TJH Hele, MJ Willatt, A Muolo, SC Althorpe The Journal of Chemical Physics 142 (19), 2015 | 137 | 2015 |
| Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements MJ Willatt, F Musil, M Ceriotti Physical Chemistry Chemical Physics 20 (47), 29661-29668, 2018 | 135 | 2018 |
| Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties J Nigam, MJ Willatt, M Ceriotti The Journal of Chemical Physics 156 (1), 2022 | 64 | 2022 |
| Efficient implementation of atom-density representations F Musil, M Veit, A Goscinski, G Fraux, MJ Willatt, M Stricker, T Junge, ... The Journal of Chemical Physics 154 (11), 2021 | 58 | 2021 |
| Path-integral dynamics of water using curvilinear centroids G Trenins, MJ Willatt, SC Althorpe The Journal of Chemical Physics 151 (5), 2019 | 56 | 2019 |
| Approximating Matsubara dynamics using the planetary model: Tests on liquid water and ice MJ Willatt, M Ceriotti, SC Althorpe The Journal of chemical physics 148 (10), 2018 | 34 | 2018 |
| Machine learning of atomic-scale properties based on physical principles M Ceriotti, MJ Willatt, G Csányi Handbook of Materials Modeling: Methods: Theory and Modeling, 1911-1937, 2020 | 30 | 2020 |
| Atomic-scale representation and statistical learning of tensorial properties A Grisafi, DM Wilkins, MJ Willatt, M Ceriotti Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and …, 2019 | 14 | 2019 |
| Machine-learning of atomic-scale properties based on physical principles G Csányi, MJ Willatt, M Ceriotti Machine learning meets quantum physics, 99-127, 2020 | 13 | 2020 |
| Machine learning in chemistry: data-driven algorithms, learning systems, and predictions A Grisafi, D Wilkins, M Willatt, M Ceriotti American Chemical Society, 2019 | 13 | 2019 |
| Comment on “Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions”[J. Chem. Phys. 156, 034302 (2022)] SN Pozdnyakov, MJ Willatt, AP Bartók, C Ortner, G Csányi, M Ceriotti The Journal of Chemical Physics 157 (17), 2022 | 9 | 2022 |
| Matsubara dynamics and its practical implementation MJ Willatt | 8 | 2017 |
| A data-driven construction of the periodic table of the elements MJ Willatt, F Musil, M Ceriotti arXiv preprint arXiv:1807.00236, 2018 | 7 | 2018 |
| Theory and practice of atom-density representations for machine learning MJ Willatt, F Musil, M Ceriotti arXiv preprint, 2018 | 4 | 2018 |
| The direct role of nuclear motion in spin–orbit coupling in strongly correlated spin systems MJ Willatt, A Alavi The Journal of Chemical Physics 160 (23), 2024 | | 2024 |
| Physics-based machine learning for materials and molecules M Ceriotti, E Engel, M Willatt Abstracts Of Papers Of The American Chemical Society 257, 2019 | | 2019 |