Telefono
+39 010 2897 439
Research center
CHT@Erzelli
All Publications
2024
Faran M., Ray D., Nag S., Raucci U., Parrinello M., Bisker G.
A Stochastic Landscape Approach for Protein Folding State Classification
Journal of Chemical Theory and Computation
Article
Journal
2024
Ruiz Munevar M.J., Rizzi V., Portioli C., Vidossich P., Cao E., Parrinello M., Cancedda L., De Vivo M.
Cation Chloride Cotransporter NKCC1 Operates through a Rocking-Bundle Mechanism
Journal of the American Chemical Society
2024
Kang P., Trizio E., Parrinello M.
Computing the committor with the committor to study the transition state ensemble
Nature Computational Science, vol. 4, (no. 6), pp. 451-460
Article
Journal
2024
Ray D., Parrinello M.
Data-driven classification of ligand unbinding pathways
Proceedings of the National Academy of Sciences of the United States of America, vol. 121, (no. 10)
2024
Mullender L., Rizzi A., Parrinello M., Carloni P., Mandelli D.
Effective data-driven collective variables for free energy calculations from metadynamics of paths
PNAS Nexus, vol. 3, (no. 4)
Article
Journal
Scientific Talks
2024
Trizio E., Kang P., Parrinello M.
Computing the committor using the committor to study the transitions state ensemble
CSI workshop 2024 - Princeton University
Workshop/Symposium
2021
Parrinello M.
Artificial Intelligence meets Atomistic Simulations
Summit on Artificial Intelligence G20 Event
Workshop/Symposium
2021
Parrinello M.
Keynote Speaker
INM-IBI Retreat
Workshop/Symposium
2021
Parrinello M.
Machine Learning and Molecular Dynamics
Thomas Young Centre 15th Anniversary
Workshop/Symposium
2021
Parrinello M.
Machine Learning and Molecular Dynamics
Innovative Strategies for Neurodegenerative Diseases CECAM Pisa
Workshop/Symposium
Oral presentations
2021
Bonati L., Parrinello M.
Building machine learning potentials for reactive events using enhanced sampling methods
ML-IP Psi-k Young & Early Career Researchers' Workshop
Conference
Colleagues of Atomistic Simulations
