Dr. Piero Gasparotto
After obtaining a M.Sc. in Materials Science at the University of Padua, Piero Gasparotto joined the group of Prof. Michele Ceriotti at École Polytechnique Fédérale de Lausanne, where he worked on developing new methodologies for pattern recognition in atomistic simulations. He also studied hydrogen bonding in water and biomolecules, structure and kinetics of supramolecular polymers and polymorphism of molecular crystals. After graduation, he moved to University College London joining the group of Prof. Angelos Michaelides in the Department of Physics and Astronomy. His research work in London focused on understanding dynamical heterogeneity in supercooled water and how structural correlations are affected by the presence of interfaces.
In 2019 he joined Empa where is currently a postdoctoral researcher under the supervision of Dr. Carlo Pignedoli working on the development of new machine-learning approaches for a better understanding and faster design of new low-dimensional graphene-based nanomaterials.
Fields of interest
machine learning, atomistic simulations, nanomaterials, water structure and dynamics, supramolecular polymers, molecular Crystals
P. Gasparotto and M. Ceriotti, Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond, J. Chem. Phys. 141, 174110 (2014) DOI: 10.1063/1.4900655
P. Gasparotto, Ali A. Hassanali, M. Ceriotti, Probing Defects and Correlations in the Hydrogen-Bond Network of ab Initio Water, J. Chem. Theory Comput. 12, 1953 (2016) DOI: 10.1021/acs.jctc.5b01138
P. Gasparotto, M.Rossi, M. Ceriotti, Anharmonic and quantum fluctuations in molecular crystals: a first-principles study of the stability of paracetamol, Phys. Rev. Lett. 117, 115702 (2016) DOI: 10.1103/PhysRevLett.117.115702
P. Gasparotto, R. H. Meißner and M. Ceriotti, Recognizing Local and Global Structural Motifs At the Atomic Scale, J. Chem. Theory Comput. 14 (2), 486 (2018) DOI: 10.1021/acs.jctc.7b00993
B.A. Helfrecht, P. Gasparotto, F. Giberti, M. Ceriotti, Atomic Motif Recognition in (Bio) Polymers: Benchmarks from the Protein Data Bank, Front Mol Biosci 6, 24 (2019) DOI: 10.3389/fmolb.2019.00024