Publications
2025
Das S., Raucci U., Trizio E., Kang P., Neves R.P., Ramos M.J., Parrinello M.
A Machine Learning-Driven, Probability-Based Approach to Enzyme Catalysis
ACS Catalysis, vol. 15, (no. 11), pp. 9785-9792
2025
Parrinello M., Greed S.
Discussions with a dignitary of molecular dynamics
Nature Reviews Chemistry, vol. 9, (no. 9)
2025
Trizio E., Kang P., Parrinello M.
Everything everywhere all at once: a probability-based enhanced sampling approach to rare events
Nature Computational Science
2025
Novelli P., Meanti G., Buigues P., Rosasco L., Parrinello M., Pontil M., Bonati L.
Fast and Fourier Features for Transfer Learning of Interatomic Potentials
npj Computational Materials
Article
Journal
2025
Tosello Gardini A., Raucci U., Parrinello M.
Machine learning-driven molecular dynamics unveils a bulk phase transformation driving ammonia synthesis on barium hydride
Nature Communications, vol. 16, (no. 1)
2025
Tribello G.A., Bonomi M., Bussi G., Camilloni C., Armstrong B.I., Arsiccio A., Aureli S., Ballabio F., Bernetti M., Bonati L., Brookes S.G.H., Brotzakis Z.F., Capelli R., Ceriotti M., Chan K.-T., Cossio P., Dasetty S., Donadio D., Ensing B., Ferguson A.L., Fraux G., Gale J.D., Gervasio F.L., Giorgino T., Herringer N.S.M., Hocky G.M., Hoff S.E., Invernizzi M., Languin-Cattoen O., Leone V., Limongelli V., Lopez-Acevedo O., Marinelli F., Febrer Martinez P., Masetti M., Mehdi S., Michaelides A., Murtada M.H., Parrinello M., Piaggi P.M., Pietropaolo A., Pietrucci F., Pipolo S., Pritchard C., Raiteri P., Raniolo S., Rapetti D., Rizzi V., Rydzewski J., Salvalaglio M., Schran C., Seal A., Shayesteh Zadeh A., Silva T.F.D., Spiwok V., Stirnemann G., Sucerquia D., Tiwary P., Valsson O., Vendruscolo M., Voth G.A., White A.D., Wu J.
PLUMED Tutorials: A collaborative, community-driven learning ecosystem
Journal of Chemical Physics, vol. 162, (no. 9)
2025
Devergne T., Kostic V., Pontil M., Parrinello M.
Slow dynamical modes from static averages
Journal of Chemical Physics, vol. 162, (no. 12)
2025
Amaro R.E., Aqvist J., Bahar I., Battistini F., Bellaiche A., Beltran D., Biggin P.C., Bonomi M., Bowman G.R., Bryce R.A., Bussi G., Carloni P., Case D.A., Cavalli A., Chang C.-E.A., Cheatham T.E., Cheung M.S., Chipot C., Chong L.T., Choudhary P., Cisneros G.A., Clementi C., Collepardo-Guevara R., Coveney P., Covino R., Crawford T.D., Dal Peraro M., de Groot B.L., Delemotte L., De Vivo M., Essex J.W., Fraternali F., Gao J., Gelpi J.L., Gervasio F.L., Gonzalez-Nilo F.D., Grubmuller H., Guenza M.G., Guzman H.V., Harris S., Head-Gordon T., Hernandez R., Hospital A., Huang N., Huang X., Hummer G., Iglesias-Fernandez J., Jensen J.H., Jha S., Jiao W., Jorgensen W.L., Kamerlin S.C.L., Khalid S., Laughton C., Levitt M., Limongelli V., Lindahl E., Lindorff-Larsen K., Loverde S., Lundborg M., Luo Y.L., Luque F.J., Lynch C.I., MacKerell A.D., Magistrato A., Marrink S.J., Martin H., McCammon J.A., Merz K., Moliner V., Mulholland A.J., Murad S., Naganathan A.N., Nangia S., Noe F., Noy A., Olah J., O'Mara M.L., Ondrechen M.J., Onuchic J.N., Onufriev A., Osuna S., Palermo G., Panchenko A.R., Pantano S., Parish C., Parrinello M., Perez A., Perez-Acle T., Perilla J.R., Pettitt B.M., Pietropaolo A., Piquemal J.-P., Poma A.B., Praprotnik M., Ramos M.J., Ren P., Reuter N., Roitberg A., Rosta E., Rovira C., Roux B., Rothlisberger U., Sanbonmatsu K.Y., Schlick T., Shaytan A.K., Simmerling C., Smith J.C., Sugita Y., Swiderek K., Taiji M., Tao P., Tieleman D.P., Tikhonova I.G., Tirado-Rives J., Tunon I., van der Kamp M.W., van der Spoel D., Velankar S., Voth G.A., Wade R., Warshel A., Welborn V.V., Wetmore S.D., Wheeler T.J., Wong C.F., Yang L.-W., Zacharias M., Orozco M.
The need to implement FAIR principles in biomolecular simulations
Nature Methods, vol. 22, (no. 4), pp. 641-645
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
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
2024
Das S., Raucci U., Neves R.P.P., Ramos M.J., Parrinello M.
Correlating enzymatic reactivity for different substrates using transferable data-driven collective variables
Proceedings of the National Academy of Sciences of the United States of America, vol. 121, (no. 49)
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
Zhang J., Bonati L., Trizio E., Zhang O., Kang Y., Hou T., Parrinello M.
Descriptor-Free Collective Variables from Geometric Graph Neural Networks
Journal of Chemical Theory and Computation, vol. 20, (no. 24), pp. 10787-10797
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)