Michele Ceriotti

发表

A self-learning algorithm for biased molecular dynamics

Michele Parrinello, Gareth A Tribello, Michele Ceriotti, 2010, Proceedings of the National Academy of Sciences.

Using sketch-map coordinates to analyze and bias molecular dynamics simulations

Michele Parrinello, Gareth A Tribello, Michele Ceriotti, 2012, Proceedings of the National Academy of Sciences.

i-PI 2.0: A universal force engine for advanced molecular simulations

Steven Vandenbrande, Wei Fang, Michele Ceriotti, 2018, Comput. Phys. Commun..

Beyond static structures: Putting forth REMD as a tool to solve problems in computational organic chemistry

Michele Ceriotti, Riccardo Petraglia, Adrien Nicolaï, 2015, J. Comput. Chem..

Incompleteness of Atomic Structure Representations.

Christoph Ortner, Michele Ceriotti, Sergey N. Pozdnyakov, 2020, Physical review letters.

Recognizing Local and Global Structural Motifs at the Atomic Scale.

Michele Ceriotti, Piero Gasparotto, Robert Horst Meißner, 2018, Journal of chemical theory and computation.

The role of feature space in atomistic learning

Michele Ceriotti, Alexander Goscinski, Guillaume Fraux, 2020, Mach. Learn. Sci. Technol..

i-PI: A Python interface for ab initio path integral molecular dynamics simulations

Michele Ceriotti, Joshua More, David E. Manolopoulos, 2014, Comput. Phys. Commun..

Efficient stochastic thermostatting of path integral molecular dynamics.

Michele Parrinello, Michele Ceriotti, Thomas E Markland, 2010, The Journal of chemical physics.

Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond.

Michele Ceriotti, Piero Gasparotto, M. Ceriotti, 2014, The Journal of chemical physics.

Iterative unbiasing of quasi-equilibrium sampling.

Michele Ceriotti, Federico Giberti, Bingqing Cheng, 2019, Journal of chemical theory and computation.

Physics-based machine learning for materials and molecules

Michele Ceriotti, Michael J. Willatt, Edgar A. Engel, 2019 .

Machine learning properties of materials and molecules with entropy-regularized kernels

Michele Ceriotti, Gábor Csányi, Albert P. Bartók, 2017 .

Demonstrating the Transferability and the Descriptive Power of Sketch-Map.

Michele Parrinello, Gareth A Tribello, Michele Ceriotti, 2013, Journal of chemical theory and computation.

Structure-Property Maps with Kernel Principal Covariates Regression pdf

Michele Ceriotti, Benjamin A. Helfrecht, Guillaume Fraux, 2020, ArXiv.

Analysing and Rationalising Molecular and Materials Databases Using Machine-Learning

Michele Ceriotti, Sandip De, 2017 .

Discussion: measurement and instrumentation

A G Seel, Roberto Senesi, Felix Fernandez-Alonso, 2014 .

Nuclear quantum effects enter the mainstream

Michele Ceriotti, Thomas E. Markland, M. Ceriotti, 2018, 1803.01037.

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials.

Jörg Behler, Michele Ceriotti, Giulio Imbalzano, 2018, The Journal of chemical physics.

High order path integrals made easy.

Jörg Behler, Michele Ceriotti, Venkat Kapil, 2016, The Journal of chemical physics.

Promoting transparency and reproducibility in enhanced molecular simulations

Jakub Rydzewski, Toni Giorgino, Massimiliano Bonomi, 2019, Nature Methods.

Atomic Motif Recognition in (Bio)Polymers: Benchmarks From the Protein Data Bank

Michele Ceriotti, Benjamin A. Helfrecht, Piero Gasparotto, 2019, Front. Mol. Biosci..

Multi-scale approach for the prediction of atomic scale properties

Andrea Grisafi, Michele Ceriotti, Jigyasa Nigam, 2020, Chemical science.

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning.

Michele Ceriotti, Michael J. Willatt, Félix Musil, 2018, Journal of chemical theory and computation.

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

Andrea Grisafi, Michele Ceriotti, David M. Wilkins, 2019, ACS Symposium Series.

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.

Andrea Grisafi, Michele Ceriotti, Gábor Csányi, 2017, Physical review letters.

Chemiscope: interactive structure-property explorer for materials and molecules

Michele Ceriotti, Guillaume Fraux, Rose K. Cersonsky, 2020, J. Open Source Softw..

Machine learning unifies the modeling of materials and molecules

Noam Bernstein, James R Kermode, Michele Ceriotti, 2017, Science Advances.

The inefficiency of re-weighted sampling and the curse of system size in high-order path integration

Oliver Riordan, Michele Ceriotti, David E. Manolopoulos, 2011, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

Unsupervised machine learning in atomistic simulations, between predictions and understanding.

Michele Ceriotti, M. Ceriotti, 2019, The Journal of chemical physics.

Uncertainty estimation for molecular dynamics and sampling.

Michele Ceriotti, Venkat Kapil, Giulio Imbalzano, 2021, The Journal of chemical physics.

The δ-thermostat: selective normal-modes excitation by colored-noise Langevin dynamics

Michele Parrinello, Michele Ceriotti, M. Parrinello, 2010 .

Improving sample and feature selection with principal covariates regression pdf

Michele Ceriotti, Benjamin A. Helfrecht, Edgar A. Engel, 2020, Mach. Learn. Sci. Technol..

Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems.

Fabio Pietrucci, Alexandre Tkatchenko, Andrew L. Ferguson, 2020, Journal of chemical theory and computation.

Simplifying the representation of complex free-energy landscapes using sketch-map

Michele Parrinello, Gareth A Tribello, Michele Ceriotti, 2011, Proceedings of the National Academy of Sciences.

Machine Learning at the Atomic Scale.

Michele Ceriotti, Félix Musil, 2019, Chimia.

Uncertainty estimation by committee models for molecular dynamics and thermodynamic averages pdf

Michele Ceriotti, Venkat Kapil, Giulio Imbalzano, 2020, ArXiv.

Mapping and classifying molecules from a high-throughput structural database

Michele Ceriotti, Carsten Baldauf, Sandip De, 2016, Journal of Cheminformatics.

A new kind of atlas of zeolite building blocks.

Michele Ceriotti, Benjamin A. Helfrecht, Scott M. Auerbach, 2019, The Journal of chemical physics.

Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties.

Michele Ceriotti, Michael J. Willatt, Jigyasa Nigam, 2021, The Journal of chemical physics.

Optimal radial basis for density-based atomic representations

Michele Ceriotti, Alexander Goscinski, Sergey Pozdnyakov, 2021, The Journal of chemical physics.

The role of feature space in atomistic learning

Michele Ceriotti, Alexander Goscinski, Guillaume Fraux, 2021, Mach. Learn. Sci. Technol..

Incompleteness of graph convolutional neural networks for points clouds in three dimensions pdf

Michele Ceriotti, Sergey N. Pozdnyakov, S. Pozdnyakov, 2022, ArXiv.

Anharmonic and Quantum Fluctuations in Molecular Crystals: A First-Principles Study of the Stability of Paracetamol.

Michele Ceriotti, Mariana Rossi, Piero Gasparotto, 2016, Physical review letters.

Communication: On the consistency of approximate quantum dynamics simulation methods for vibrational spectra in the condensed phase.

Michele Ceriotti, Mariana Rossi, Francesco Paesani, 2014, The Journal of chemical physics.

Decisive role of nuclear quantum effects on surface mediated water dissociation at finite temperature.

Michele Ceriotti, Mariana Rossi, Yair Litman, 2017, The Journal of chemical physics.

Colored-Noise Thermostats à la Carte

Giovanni Bussi, Michele Parrinello, Michele Ceriotti, 2010, 1204.0822.

Fine tuning classical and quantum molecular dynamics using a generalized Langevin equation.

Michele Ceriotti, Venkat Kapil, Mariana Rossi, 2017, The Journal of chemical physics.

Static disorder and structural correlations in the low-temperature phase of lithium imide

Michele Parrinello, Michele Ceriotti, Giacomo Miceli, 2011 .

Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations.

Jörg Behler, Michele Ceriotti, Matti Hellström, 2018, The journal of physical chemistry. B.

Mapping uncharted territory in ice from zeolite networks to ice structures

Michele Ceriotti, Chris J. Pickard, Richard J. Needs, 2018, Nature Communications.

Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CH3SO3H and H2O2 in phenol.

Clémence Corminboeuf, Michele Ceriotti, Kevin Rossi, 2020, Journal of chemical theory and computation.

Solid-liquid interfacial free energy out of equilibrium

Gareth A. Tribello, Michele Ceriotti, Bingqing Cheng, 2015, 1511.08668.

Chemical shifts in molecular solids by machine learning

Michele Ceriotti, Lyndon Emsley, Edgar A. Engel, 2018, Nature Communications.

Fast-forward Langevin dynamics with momentum flips.

Michele Ceriotti, David M. Wilkins, Mahdi Hijazi, 2018, The Journal of chemical physics.

The fuzzy quantum proton in the hydrogen chloride hydrates.

Michele Parrinello, Michele Ceriotti, Chris J Pickard, 2012, Journal of the American Chemical Society.

Quantum fluctuations and isotope effects in ab initio descriptions of water.

Lu Wang, Michele Ceriotti, Thomas E. Markland, 2014, The Journal of chemical physics.

A Data-Driven Construction of the Periodic Table of the Elements

Michele Ceriotti, Michael J. Willatt, Félix Musil, 2018, 1807.00236.

A Hybrid Approach to Fermi Operator Expansion

Thomas D. Kuhne, Michele Parrinello, Michele Ceriotti, 2009 .

A Bayesian approach to NMR crystal structure determination.

Michele Ceriotti, Lyndon Emsley, Andrea Anelli, 2019, Physical chemistry chemical physics : PCCP.

Ab initio study of the vibrational properties of crystalline TeO2: The alpha, beta, and gamma phases

Fabio Pietrucci, Michele Ceriotti, Marco Bernasconi, 2006, 0803.4056.

Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals

Andrea Grisafi, Michele Ceriotti, Mariana Rossi, 2019, New Journal of Physics.

Accelerating the convergence of path integral dynamics with a generalized Langevin equation.

Michele Parrinello, Michele Ceriotti, David E Manolopoulos, 2011, The Journal of chemical physics.

An efficient and accurate decomposition of the Fermi operator.

Michele Parrinello, Michele Ceriotti, M. Parrinello, 2008, The Journal of chemical physics.

Neural network potential for Al-Mg-Si alloys

Ryo Kobayashi, William A. Curtin, Michele Ceriotti, 2017 .

Nuclear quantum effects in solids using a colored-noise thermostat.

Giovanni Bussi, Michele Parrinello, Michele Ceriotti, 2009, Physical review letters.

Langevin equation with colored noise for constant-temperature molecular dynamics simulations.

Giovanni Bussi, Michele Parrinello, Michele Ceriotti, 2008, Physical review letters.

Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

Michele Ceriotti, Josh E. Campbell, Graeme M. Day, 2018, Chemistry of Materials.

Probing Defects and Correlations in the Hydrogen-Bond Network of ab Initio Water.

Michele Ceriotti, Piero Gasparotto, Ali A. Hassanali, 2016, Journal of chemical theory and computation.

Solid-liquid interface free energy through metadynamics simulations

Michele Ceriotti, Stefano Angioletti-Uberti, Peter D. Lee, 2009, 0911.2615.

Nuclear quantum effects in ab initio dynamics: Theory and experiments for lithium imide

Michele Parrinello, Michele Ceriotti, Daniele Colognesi, 2010, 1009.2862.

Efficient multiple time scale molecular dynamics: Using colored noise thermostats to stabilize resonances.

Michele Ceriotti, Joseph A Morrone, Thomas E. Markland, 2010, The Journal of chemical physics.

Efficient methods and practical guidelines for simulating isotope effects.

Michele Ceriotti, Thomas E. Markland, Thomas E Markland, 2012, The Journal of chemical physics.

Direct Measurement of Competing Quantum Effects on the Kinetic Energy of Heavy Water upon Melting

Roberto Senesi, Carla Andreani, Michele Ceriotti, 2013, 1311.7247.

Nuclear Quantum Effects in H(+) and OH(-) Diffusion along Confined Water Wires.

Michele Ceriotti, Mariana Rossi, David E Manolopoulos, 2016, The journal of physical chemistry letters.

Atom-density representations for machine learning.

Michele Ceriotti, Michael J. Willatt, Félix Musil, 2018, The Journal of chemical physics.

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions.

Jörg Behler, Michele Ceriotti, Gábor Csányi, 2018, The Journal of chemical physics.

Machine Learning of Atomic-Scale Properties Based on Physical Principles

Michele Ceriotti, Michael J. Willatt, G'abor Cs'anyi, 2019, Handbook of Materials Modeling.

Ab initio modelling of the early stages of precipitation in Al-6000 alloys

Michele Ceriotti, Till Junge, M. Ceriotti, 2017, 1708.07908.

Semiconducting Nanowire‐Based Optoelectronic Fibers

Michele Ceriotti, Arouna Darga, Mariana Rossi, 2017, Advanced materials.

Accelerated path integral methods for atomistic simulations at ultra-low temperatures.

Michele Ceriotti, Dominik Marx, D. Marx, 2016, The Journal of chemical physics.

Electron density learning of non-covalent systems

Alberto Fabrizio, Andrea Grisafi, Michele Ceriotti, 2019, Chemical science.

Transferable Machine-Learning Model of the Electron Density

Alberto Fabrizio, Andrea Grisafi, Michele Ceriotti, 2018, ACS central science.

Nuclear quantum effects and hydrogen bond fluctuations in water

Michele Parrinello, Michele Ceriotti, David E. Manolopoulos, 2013, Proceedings of the National Academy of Sciences.

Probing the Unfolded Configurations of a β-Hairpin Using Sketch-Map.

Michele Parrinello, Gareth A Tribello, Michele Ceriotti, 2015, Journal of chemical theory and computation.

Nuclear Quantum Effects in Water at the Triple Point: Using Theory as a Link Between Experiments.

Jörg Behler, Michele Ceriotti, Bingqing Cheng, 2016, The journal of physical chemistry letters.

Anisotropy of the Proton Momentum Distribution in Water.

Michele Ceriotti, Venkat Kapil, Alice Cuzzocrea, 2018, The journal of physical chemistry. B.

Incorporating long-range physics in atomic-scale machine learning.

Andrea Grisafi, Michele Ceriotti, 2019, The Journal of chemical physics.

Direct path integral estimators for isotope fractionation ratios.

Michele Ceriotti, Bingqing Cheng, M. Ceriotti, 2014, The Journal of chemical physics.

Efficient first-principles calculation of the quantum kinetic energy and momentum distribution of nuclei.

Michele Ceriotti, David E Manolopoulos, D. Manolopoulos, 2012, Physical review letters.

Accurate molecular polarizabilities with coupled cluster theory and machine learning

Yang Yang, Andrea Grisafi, Michele Ceriotti, 2018, Proceedings of the National Academy of Sciences.