| 15. |
M. J. Willatt, A. Alavi,
The direct role of nuclear motion in spin-orbit coupling in strongly correlated spin systems JCP, 160, 234103 (2024); https://doi.org/10.1063/5.0209702 |
| 14. |
S. N. Pozdnyakov, M. J. Willatt, A. P. Bartok, C. Ortner, G. Csanyi, M. Ceriotti,
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)] JCP, 157, 177101 (2022); https://doi.org/10.1063/5.0088404 |
| 13. |
J. Nigam, M. J. Willatt, M. Ceriotti,
Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties, JCP, 156, 014115 (2022); https://doi.org/10.1063/5.0072784 |
| 12. |
F. Musil, M. Veit, A. Goscinski, G. Fraux, M. J. Willatt, M. Stricker, T. Junge, M. Ceriotti,
Efficient implementation of atom-density representations, JCP, 154, 114109 (2021); https://doi.org/10.1063/5.0044689 |
| 11. |
S. N. Pozdnyakov, M. J. Willatt, A. P. Bartok, C. Ortner, G. Csanyi, M. Ceriotti,
Incompleteness of atomic structure representations, PRL, 125, 166001 (2020); https://doi.org/10.1103/PhysRevLett.125.166001 |
| 10. |
G. Trenins, M. J. Willatt, S. C. Althorpe,
Path-integral dynamics using curvilinear centroids, JCP, 151, 054109 (2019); https://doi.org/10.1063/1.5100587 |
| 9. |
A. Grisafi, D. M. Wilkins, M. J. Willatt, M. Ceriotti,
Atomic-scale representation and statistical learning of tensorial properties, Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions (2019); https://doi.org/10.1021/bk-2019-1326.ch001 |
| 8. |
M. J. Willatt, F. Musil, M. Ceriotti,
Atom-density representations for machine learning, JCP, 150, 154110 (2019); https://doi.org/10.1063/1.5090481 |
| 7. |
F. Musil, M. J. Willatt, M. Ceriotti,
Fast and accurate uncertainty estimation in chemical machine learning, JCTC, 15, 906 (2019); https://doi.org/10.1021/acs.jctc.8b00959 |
| 6. |
M. J. Willatt, F. Musil, M. Ceriotti,
Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements, PCCP, 20, 29661 (2018); https://doi.org/10.1039/C8CP05921G |
| 5. |
M. Ceriotti, M. J. Willatt, G. Csanyi,
Machine learning of atomic-scale properties based on physical principles, Handbook of Materials Modeling (2018); https://doi.org/10.1007/978-3-319-42913-7_68-1 |
| 4. |
M. J. Willatt, M. Ceriotti, S. C. Althorpe,
Approximating Matsubara dynamics using the planetary model: tests on liquid water and ice, JCP, 148, 102336 (2018); https://doi.org/10.1063/1.5004808 |
| 3. |
M. J. Willatt,
Matsubara dynamics and its practical implementation, PhD thesis (2017); https://doi.org/10.17863/CAM.13644 |
| 2. |
T. J. H. Hele, M. J. Willatt, A. Muolo, S. C. Althorpe,
Communication: Relation of centroid molecular dynamics and ring-polymer molecular dynamics to exact quantum dynamics, JCP, 142, 191101 (2015); https://doi.org/10.1063/1.4921234 |
| 1. |
T. J. H. Hele, M. J. Willatt, A. Muolo, S. C. Althorpe,
Boltzmann-conserving classical dynamics in quantum time-correlation functions: Matsubara dynamics, JCP, 142, 134103 (2015); https://doi.org/10.1063/1.4916311 |