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The Journal of Chemical Physics
|
November 10, 2014
Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond
Piero Gasparotto, Michele Ceriotti
Methods in Molecular Biology (Clifton, N.J.)
|
August 10, 2019
Using Data-Reduction Techniques to Analyze Biomolecular Trajectories
Gareth A Tribello, Piero Gasparotto
Frontiers in Molecular Biosciences
|
July 6, 2019
Using Dimensionality Reduction to Analyze Protein Trajectories
Gareth A Tribello, Piero Gasparotto
Physical Review Letters
|
September 24, 2016
Anharmonic and Quantum Fluctuations in Molecular Crystals: A First-Principles Study of the Stability of Paracetamol
Mariana Rossi, Piero Gasparotto, Michele Ceriotti
Journal of Chemical Theory and Computation
|
February 17, 2016
Probing Defects and Correlations in the Hydrogen-Bond Network of ab Initio Water
Piero Gasparotto, Ali A Hassanali, Michele Ceriotti
Journal of Chemical Theory and Computation
|
January 4, 2018
Recognizing Local and Global Structural Motifs at the Atomic Scale
Piero Gasparotto, Robert Horst Meißner, Michele Ceriotti
The Journal of Physical Chemistry. B
|
January 1, 2020
Identifying and Tracking Defects in Dynamic Supramolecular Polymers
Piero Gasparotto, Davide Bochicchio, Michele Ceriotti, et al.
Frontiers in Molecular Biosciences
|
May 7, 2019
Atomic Motif Recognition in (Bio)Polymers: Benchmarks From the Protein Data Bank
Benjamin A Helfrecht, Piero Gasparotto, Federico Giberti, et al.
The Journal of Chemical Physics
|
April 23, 2022
Erratum: "An accurate and transferable machine learning potential for carbon" [J. Chem. Phys. 153, 034702 (2020)]
Patrick Rowe, Volker L Deringer, Piero Gasparotto, et al.
The Journal of Chemical Physics
|
July 28, 2020
An accurate and transferable machine learning potential for carbon
Patrick Rowe, Volker L Deringer, Piero Gasparotto, et al.
Page
of 2
Search research articles
Search
Showing results (1-10 of 18) with videos related to
Sort By:
Page
of 2
The Journal of Chemical Physics
|
November 10, 2014
Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond
Piero Gasparotto, Michele Ceriotti
Methods in Molecular Biology (Clifton, N.J.)
|
August 10, 2019
Using Data-Reduction Techniques to Analyze Biomolecular Trajectories
Gareth A Tribello, Piero Gasparotto
Frontiers in Molecular Biosciences
|
July 6, 2019
Using Dimensionality Reduction to Analyze Protein Trajectories
Gareth A Tribello, Piero Gasparotto
Physical Review Letters
|
September 24, 2016
Anharmonic and Quantum Fluctuations in Molecular Crystals: A First-Principles Study of the Stability of Paracetamol
Mariana Rossi, Piero Gasparotto, Michele Ceriotti
Journal of Chemical Theory and Computation
|
February 17, 2016
Probing Defects and Correlations in the Hydrogen-Bond Network of ab Initio Water
Piero Gasparotto, Ali A Hassanali, Michele Ceriotti
Journal of Chemical Theory and Computation
|
January 4, 2018
Recognizing Local and Global Structural Motifs at the Atomic Scale
Piero Gasparotto, Robert Horst Meißner, Michele Ceriotti
The Journal of Physical Chemistry. B
|
January 1, 2020
Identifying and Tracking Defects in Dynamic Supramolecular Polymers
Piero Gasparotto, Davide Bochicchio, Michele Ceriotti, et al.
Frontiers in Molecular Biosciences
|
May 7, 2019
Atomic Motif Recognition in (Bio)Polymers: Benchmarks From the Protein Data Bank
Benjamin A Helfrecht, Piero Gasparotto, Federico Giberti, et al.
The Journal of Chemical Physics
|
April 23, 2022
Erratum: "An accurate and transferable machine learning potential for carbon" [J. Chem. Phys. 153, 034702 (2020)]
Patrick Rowe, Volker L Deringer, Piero Gasparotto, et al.
The Journal of Chemical Physics
|
July 28, 2020
An accurate and transferable machine learning potential for carbon
Patrick Rowe, Volker L Deringer, Piero Gasparotto, et al.
Page
of 2