Michele Ceriotti
michele.ceriotti@epfl.ch +41 21 693 29 39 http://cosmo.epfl.ch/
EPFL STI IMX COSMO
MXG 337 (Bâtiment MXG)
Station 12
CH-1015 Lausanne
+41 21 693 29 39
+41 21 693 29 25
Office: MXG 337
EPFL > STI > IMX > COSMO
Web site: Web site: https://cosmo.epfl.ch/
Fields of expertise
Atomistic computer simulations, statistical mechanics, machine learning, molecular dynamics, nuclear quantum effects, molecular materials, nucleation.
Biography
Michele Ceriotti received his Ph.D. in Physics from ETH Zürich in 2010. He spent three years in Oxford as a Junior Research Fellow at Merton College. Since 2013 he leads the laboratory for Computational Science and Modeling in the Institute of Materials at EPFL. His research revolves around the atomic-scale modelling of materials, based on the sampling of quantum and thermal fluctuations and on the use of machine learning to predict and rationalize structure-property relations. He has been awarded the IBM Research Forschungspreis in 2010, the Volker Heine Young Investigator Award in 2013, an ERC Starting Grant in 2016, and the IUPAP C10 Young Scientist Prize in 2018.
Publications
Infoscience publications
2020
B. Cheng; M. Ceriotti; G. A. Tribello : Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification; Journal Of Chemical Physics. 2020-01-31. DOI : 10.1063/1.5134461.
M. Asgari; R. Semino; P. A. Schouwink; I. Kochetygov; J. Tarver et al. : Understanding How Ligand Functionalization Influences CO2 and N-2 Adsorption in a Sodalite Metal-Organic Framework; Chemistry Of Materials. 2020-02-25. DOI : 10.1021/acs.chemmater.9b04631.
I. Poltaysky; V. Kapil; M. Ceriotti; K. S. Kim; A. Tkatchenko : Accurate Description of Nuclear Quantum Effects with High-Order Perturbed Path Integrals (HOPPI); Journal Of Chemical Theory And Computation. 2020-02-01. DOI : 10.1021/acs.jctc.9b00881.
P. Gasparotto; D. Bochicchio; M. Ceriotti; G. M. Pavan : Identifying and Tracking Defects in Dynamic Supramolecular Polymers; Journal Of Physical Chemistry B. 2020-01-23. DOI : 10.1021/acs.jpcb.9b11015.
F. Giberti; B. Cheng; G. A. Tribello; M. Ceriotti : Iterative Unbiasing of Quasi-Equilibrium Sampling; Journal Of Chemical Theory And Computation. 2020-01-01. DOI : 10.1021/acs.jctc.9b00907.
2019
F. Musil; M. Ceriotti : Machine Learning at the Atomic Scale; Chimia. 2019-12-01. DOI : 10.2533/chimia.2019.972.
Z. Luo; A. Murello; D. M. Wilkins; F. Kovacik; J. Kohlbrecher et al. : Determination and evaluation of the nonadditivity in wetting of molecularly heterogeneous surfaces; Proceedings of the National Academy of Sciences. 2019-12-17. DOI : 10.1073/pnas.1916180116.
A. Grisafi; D. M. Wilkins; M. J. Willatt; M. Ceriotti : Atomic-Scale Representation and Statistical Learning of Tensorial Properties; Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions; Cornell University, 2019. p. 1-21.
A. Grisafi; M. Ceriotti : Incorporating long-range physics in atomic-scale machine learning; The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5128375.
V. Kapil; E. A. Engel; M. Rossi; M. Ceriotti : Assessment of Approximate Methods for Anharmonic Free Energies; Journal of Chemical Theory and Computation. 2019. DOI : 10.1021/acs.jctc.9b00596.
E. A. Engel; A. Anelli; A. Hofstetter; F. M. Paruzzo; L. Emsley et al. : A Bayesian approach to NMR crystal structure determination; Physical Chemistry Chemical Physics. 2019. DOI : 10.1039/C9CP04489B.
B. A. Helfrecht; R. Semino Barbaresi; G. Pireddu; S. M. Auerbach; M. Ceriotti : A new kind of atlas of zeolite building blocks; The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5119751.
B. A. Helfrecht; P. Gasparotto; F. Giberti; M. Ceriotti : Atomic Motif Recognition in (Bio)Polymers: Benchmarks From the Protein Data Bank; Frontiers in Molecular Biosciences. 2019. DOI : 10.3389/fmolb.2019.00024.
M. Liu; L. Zhang; M. A. Little; V. Kapil; M. Ceriotti et al. : Barely porous organic cages for hydrogen isotope separation; Science. 2019. DOI : 10.1126/science.aax7427.
M. Ceriotti : Unsupervised machine learning in atomistic simulations, between predictions and understanding; The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5091842.
M. Asgari; R. Semino; P. Schouwink; I. Kochetygov; O. Trukhina et al. : An In-Situ Neutron Diffraction and DFT Study of Hydrogen Adsorption in a Sodalite-Type Metal-Organic Framework, Cu-BTTri; European Journal of Inorganic Chemistry. 2019. DOI : 10.1002/ejic.201801253.
M. J. Willatt; F. Musil; M. Ceriotti : Atom-density representations for machine learning; The Journal of Chemical Physics. 2019. DOI : 10.1063/1.5090481.
M. J. Willatt; F. Musil; M. Ceriotti; M. A. Langovoy : Fast and Accurate Uncertainty Estimation in Chemical Machine Learning; Journal of Chemical Theory and Computation. 2019. DOI : 10.1021/acs.jctc.8b00959.
N. Raimbault; A. Grisafi; M. Ceriotti; M. Rossi : Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals; New Journal of Physics. 2019. DOI : 10.1088/1367-2630/ab4509.
A. Fabrizio; A. Grisafi; B. Meyer; M. Ceriotti; C. Corminboeuf : Electron density learning of non-covalent systems; Chemical Science. 2019-11-07. DOI : 10.1039/c9sc02696g.
V. Kapil; J. Wieme; S. Vandenbrande; A. Lamaire; V. Van Speybroeck et al. : Modeling the Structural and Thermal Properties of Loaded Metal–Organic Frameworks. An Interplay of Quantum and Anharmonic Fluctuations; Journal of Chemical Theory and Computation. 2019. DOI : 10.1021/acs.jctc.8b01297.
254313; A. Grisafi; Y. Yang; K. U. Lao; R. A. DiStasio et al. : Accurate molecular polarizabilities with coupled cluster theory and machine learning; Proceedings of the National Academy of Sciences. 2019. DOI : 10.1073/pnas.1816132116.
Y. Yang; K. U. Lao; D. M. Wilkins; A. Grisafi; M. Ceriotti et al. : Quantum mechanical static dipole polarizabilities in the QM7b and AlphaML showcase databases; Scientific Data. 2019-08-19. DOI : 10.1038/s41597-019-0157-8.
M. Ceriotti; E. Engel; M. Willatt : Physics-based machine learning for materials and molecules. 2019-03-31. National Meeting of the American-Chemical-Society (ACS), Orlando, FL, Mar 31-Apr 04, 2019.
R. Dettori; M. Ceriotti; J. Hunger; L. Colombo; D. Donadio : Energy Relaxation and Thermal Diffusion in Infrared Pump-Probe Spectroscopy of Hydrogen-Bonded Liquids; Journal Of Physical Chemistry Letters. 2019-06-20. DOI : 10.1021/acs.jpclett.9b01272.
V. Kapil; M. Rossi; O. Marsalek; R. Petraglia; Y. Litman et al. : i-PI 2.0: A universal force engine for advanced molecular simulations; Computer Physics Communications. 2019-03-01. DOI : 10.1016/j.cpc.2018.09.020.
A. Grisafi; A. Fabrizio; B. Meyer; D. M. Wilkins; C. Corminboeuf et al. : Transferable Machine-Learning Model of the Electron Density; Acs Central Science. 2019-01-23. DOI : 10.1021/acscentsci.8b00551.
B. Cheng; E. A. Engel; J. Behler; C. Dellago; M. Ceriotti : Ab initio thermodynamics of liquid and solid water; Proceedings Of The National Academy Of Sciences Of The United States Of America. 2019-01-22. DOI : 10.1073/pnas.1815117116.
2018
M. Hellström; M. Ceriotti; J. Behler : Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations; The Journal of Physical Chemistry B. 2018. DOI : 10.1021/acs.jpcb.8b06433.
J. Yang; S. De; J. E. Campbell; S. Li; M. Ceriotti et al. : Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction; Chemistry of Materials. 2018. DOI : 10.1021/acs.chemmater.8b01621.
F. M. Paruzzo; A. Hofstetter; F. Musil; S. De; M. Ceriotti et al. : Chemical shifts in molecular solids by machine learning; Nature Communications. 2018. DOI : 10.1038/s41467-018-06972-x.
A. Anelli; E. A. Engel; C. J. Pickard; M. Ceriotti : Generalized convex hull construction for materials discovery; Physical Review Materials. 2018. DOI : 10.1103/PhysRevMaterials.2.103804.
T. T. Nguyen; E. Székely; G. Imbalzano; J. Behler; G. Csányi et al. : Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions; The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5024577.
Y. Litman; D. Donadio; M. Ceriotti; M. Rossi : Decisive role of nuclear quantum effects on surface mediated water dissociation at finite temperature; The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5002537.
M. J. Willatt; F. Musil; M. Ceriotti : Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements; Physical Chemistry Chemical Physics. 2018. DOI : 10.1039/C8CP05921G.
M. J. Willatt; M. Ceriotti; S. C. Althorpe : Approximating Matsubara dynamics using the planetary model: Tests on liquid water and ice; The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5004808.
B. Cheng; A. T. Paxton; M. Ceriotti : Hydrogen Diffusion and Trapping in α-Iron: The Role of Quantum and Anharmonic Fluctuations; Physical Review Letters. 2018. DOI : 10.1103/PhysRevLett.120.225901.
Y. Chen; H. I. Okur; N. Dupertuis; J. Dedic; D. M. Wilkins et al. : Comment on "Water- water correlations in electrolyte solutions probed by hyper-Rayleigh scattering" [J. Chem. Phys. 147, 214505 (2017)]; Journal Of Chemical Physics. 2018-10-28. DOI : 10.1063/1.5023579.
Thuong Nguyen; E. Szekely; G. Imbalzano; J. Behler; G. Csanyi et al. : Data-driven many-body representations with chemical accuracy for molecular simulations from the gas to the condensed phase. 2018-08-19. 256th National Meeting and Exposition of the American-Chemical-Society (ACS) - Nanoscience, Nanotechnology and Beyond, Boston, MA, Aug 19-23, 2018.
B. Cheng; C. Dellago; M. Ceriotti : Theoretical prediction of the homogeneous ice nucleation rate: disentangling thermodynamics and kinetics; Physical Chemistry Chemical Physics. 2018-12-07. DOI : 10.1039/c8cp04561e.
M. Groenonboom; S. De; M. Ceriotti; J. Keith : Applications of machine learning for studying local solvation environments. 2018. 255th National Meeting and Exposition of the American-Chemical-Society (ACS) - Nexus of Food, Energy, and Water, New Orleans, LA, Mar 18-22, 2018.
B. Cheng; M. Ceriotti : Communication: Computing the Tolman length for solid-liquid interfaces; The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5038396.
E. A. Engel; A. Anelli; M. Ceriotti; C. J. Pickard; R. J. Needs : Mapping uncharted territory in ice from zeolite networks to ice structures; Nature Communications. 2018. DOI : 10.1038/s41467-018-04618-6.
V. Kapil; A. Cuzzocrea; M. Ceriotti : Anisotropy of the Proton Momentum Distribution in Water; The Journal of Physical Chemistry. 2018. DOI : 10.1021/acs.jpcb.8b03896.
M. Hijazi; D. M. Wilkins; M. Ceriotti : Fast-forward Langevin dynamics with momentum flips; The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5029833.
G. Imbalzano; A. Anelli; D. Giofré; S. Klees; J. Behler et al. : Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials; The Journal of Chemical Physics. 2018. DOI : 10.1063/1.5024611.
L. Vannay; B. Meyer; R. Petraglia; G. Sforazzini; M. Ceriotti et al. : Analyzing Fluxional Molecules Using DORI; Journal of Chemical Theory and Computation. 2018-03-24. DOI : 10.1021/acs.jctc.7b01176.
B. Cheng; M. Ceriotti : Computing the absolute Gibbs free energy in atomistic simulations: Applications to defects in solids; Physical Review B. 2018. DOI : 10.1103/PhysRevB.97.054102.
P. Gasparotto; R. H. Meißner; M. Ceriotti : Recognizing Local and Global Structural Motifs at the Atomic Scale; Journal of Chemical Theory and Computation. 2018. DOI : 10.1021/acs.jctc.7b00993.
F. Musil; S. De; J. Yang; J. E. Campbell; G. M. Day et al. : Machine learning for the structure–energy–property landscapes of molecular crystals; Chemical Science. 2018. DOI : 10.1039/C7SC04665K.
A. Grisafi; D. M. Wilkins; G. Csányi; M. Ceriotti : Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems; Physical Review Letters. 2018. DOI : 10.1103/PhysRevLett.120.036002.
T. E. Markland; M. Ceriotti : Nuclear quantum effects enter the mainstream; Nature Reviews Chemistry. 2018. DOI : 10.1038/s41570-017-0109.
M. Rossi; V. Kapil; M. Ceriotti : Fine tuning classical and quantum molecular dynamics using a generalized Langevin equation; The Journal of Chemical Physics. 2018. DOI : 10.1063/1.4990536.
2017
A. P. Bartok; S. De; C. Poelking; N. Bernstein; J. R. Kermode et al. : Machine learning unifies the modeling of materials and molecules; Science Advances. 2017. DOI : 10.1126/sciadv.1701816.
E. Baldi; M. Ceriotti; G. A. Tribello : Extracting the interfacial free energy and anisotropy from a smooth fluctuating dividing surface; Journal Of Physics-Condensed Matter. 2017. DOI : 10.1088/1361-648X/aa893d.
B. Cheng; G. A. Tribello; M. Ceriotti : The Gibbs free energy of homogeneous nucleation: From atomistic nuclei to the planar limit; The Journal of Chemical Physics. 2017. DOI : 10.1063/1.4997180.
C. Liang; G. Tocci; D. M. Wilkins; A. Grisafi; S. Roke et al. : Solvent fluctuations and nuclear quantum effects modulate the molecular hyperpolarizability of water; Physical Review B. 2017. DOI : 10.1103/PhysRevB.96.041407.
F. Comitani; K. Rossi; M. Ceriotti; M. E. Sanz; C. Molteni : Mapping the conformational free energy of aspartic acid in the gas phase and in aqueous solution; Journal Of Chemical Physics. 2017. DOI : 10.1063/1.4979519.
D. M. Wilkins; D. E. Manolopoulos; S. Roke; M. Ceriotti : Communication: Mean-field theory of water-water correlations in electrolyte solutions; The Journal of Chemical Physics. 2017. DOI : 10.1063/1.4983221.
R. Dettori; M. Ceriotti; J. Hunger; C. Melis; L. Colombo et al. : Simulating Energy Relaxation in Pump-Probe Vibrational Spectroscopy of Hydrogen-Bonded Liquids; Journal Of Chemical Theory And Computation. 2017. DOI : 10.1021/acs.jctc.6b01108.
S. De; F. Musil; T. Ingram; C. Baldauf; M. Ceriotti : Mapping and classifying molecules from a high-throughput structural database; Journal of Cheminformatics. 2017. DOI : 10.1186/s13321-017-0192-4.
B. Cheng; M. Ceriotti : Bridging the gap between atomistic and macroscopic models of homogeneous nucleation; The Journal of Chemical Physics. 2017. DOI : 10.1063/1.4973883.
2016
V. Kapil; J. Behler; M. Ceriotti : High order path integrals made easy; The Journal of Chemical Physics. 2016. DOI : 10.1063/1.4971438.
G. Tocci; C. Liang; D. M. Wilkins; S. Roke; M. Ceriotti : Second-Harmonic Scattering as a Probe of Structural Correlations in Liquids; The Journal of Physical Chemistry Letters. 2016. DOI : 10.1021/acs.jpclett.6b01851.
M. Rossi; M. Ceriotti; D. E. Manolopoulos : Nuclear Quantum Effects in H+ and OH- Diffusion along Confined Water Wires; Journal Of Physical Chemistry Letters. 2016. DOI : 10.1021/acs.jpclett.6b01093.
F. Uhl; D. Marx; M. Ceriotti : Accelerated path integral methods for atomistic simulations at ultra-low temperatures; Journal Of Chemical Physics. 2016. DOI : 10.1063/1.4959602.
M. Ceriotti; W. Fang; P. G. Kusalik; R. H. Mckenzie; A. Michaelides et al. : Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges; Chemical Reviews. 2016. DOI : 10.1021/acs.chemrev.5b00674.
K. Voïtchovsky; D. Giofrè; J. José Segura; F. Stellacci; M. Ceriotti : Thermally-nucleated self-assembly of water and alcohol into stable structures at hydrophobic interfaces; Nature Communications. 2016. DOI : 10.1038/ncomms13064.
M. Rossi; P. Gasparotto; M. Ceriotti : Anharmonic and Quantum Fluctuations in Molecular Crystals: A First-Principles Study of the Stability of Paracetamol; Physical Review Letters. 2016. DOI : 10.1103/PhysRevLett.117.115702.
M. Ceriotti; V. Vitelli : Vitrification: Machines learn to recognize glasses; Nature Physics. 2016. DOI : 10.1038/nphys3757.
B. Cheng; J. Behler; M. Ceriotti : Nuclear Quantum Effects in Water at the Triple Point: Using Theory as a Link Between Experiments; Journal Of Physical Chemistry Letters. 2016. DOI : 10.1021/acs.jpclett.6b00729.
P. Gasparotto; A. A. Hassanali; M. Ceriotti : Probing Defects and Correlations in the Hydrogen-Bond Network of ab Initio Water; Journal of Chemical Theory and Computation. 2016. DOI : 10.1021/acs.jctc.5b01138.
S. De; A. P. Bartók; G. Csányi; M. Ceriotti : Comparing molecules and solids across structural and alchemical space; Phys. Chem. Chem. Phys.. 2016. DOI : 10.1039/C6CP00415F.
Y. Chen; H. I. Okur; N. Gomopoulos; C. Macias-Romero; P. S. Cremer et al. : Electrolytes induce long-range orientational order and free energy changes in the H-bond network of bulk water; Science Advances. 2016-04-01. DOI : 10.1126/sciadv.1501891.
V. Kapil; J. Vandevondele; M. Ceriotti : Accurate molecular dynamics and nuclear quantum effects at low cost by multiple steps in real and imaginary time: Using density functional theory to accelerate wavefunction methods; The Journal of Chemical Physics. 2016. DOI : 10.1063/1.4941091.
R. Petraglia; A. G. Nicolai; M. Wodrich; M. Ceriotti; C. Corminboeuf : Beyond Static Structures: Putting Forth REMD as a Tool to Solve Problems in Computational Organic Chemistry; Journal of Computational Chemistry. 2016. DOI : 10.1002/jcc.24025.
2015
B. Cheng; G. A. Tribello; M. Ceriotti : Solid-liquid interfacial free energy out of equilibrium; Physical Review B. 2015. DOI : 10.1103/PhysRevB.92.180102.
A. Ardevol; G. A. Tribello; M. Ceriotti; M. Parrinello : Probing the Unfolded Configurations of a β-Hairpin Using Sketch-Map; Journal of Chemical Theory and Computation. 2015. DOI : 10.1021/ct500950z.
M. Ceriotti : Modeling the Quantum Nature of Atomic Nuclei by Imaginary Time Path Integrals and Colored Noise; Computational Trends in Solvation and Transport in Liquids, Lecture Notes; Schriften des Forschungszentrums Jülich, 2015. p. 1-24.
2014
B. Cheng; M. Ceriotti : Direct path integral estimators for isotope fractionation ratios; Journal Of Chemical Physics. 2014. DOI : 10.1063/1.4904293.
M. Ceriotti; C. Drechsel-Grau; F. Fernandez-Alonso; N. Greaves; M. Krzystyniak et al. : Discussion: Theoretical Horizons and Calculation. 2014. 6th Workshop in Electron Volt Neutron Spectroscopy - Frontiers and Horizons', u'6th Workshop in Electron Volt Neutron Spectroscopy - Frontiers and Horizons']. DOI : 10.1088/1742-6596/571/1/012013.
F. Giberti; A. A. Hassanali; M. Ceriotti; M. Parrinello : The Role of Quantum Effects on Structural and Electronic Fluctuations in Neat and Charged Water; The Journal of Physical Chemistry B. 2014. DOI : 10.1021/jp507752e.
M. Ceriotti : Ab initio simulation of particle momentum distributions in high-pressure water. 2014. 6th Workshop in Electron Volt Neutron Spectroscopy: Frontiers and Horizons, Abingdon, UK, 20–21 January 2014. p. 012011. DOI : 10.1088/1742-6596/571/1/012011.
M. Rossi; H. Liu; F. Paesani; J. Bowman; M. Ceriotti : Communication: On the consistency of approximate quantum dynamics simulation methods for vibrational spectra in the condensed phase; The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4901214.
P. Gasparotto; M. Ceriotti : Recognizing molecular patterns by machine learning: An agnostic structural definition of the hydrogen bond; The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4900655.
L. Wang; M. Ceriotti; T. E. Markland : Quantum fluctuations and isotope effects in ab initio descriptions of water; The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4894287.
M. Rossi; M. Ceriotti; D. E. Manolopoulos : How to remove the spurious resonances from ring polymer molecular dynamics; The Journal of Chemical Physics. 2014. DOI : 10.1063/1.4883861.
M. Nava; M. Ceriotti; C. Dryzun; M. Parrinello : Evaluating functions of positive-definite matrices using colored-noise thermostats; Physical Review E. 2014. DOI : 10.1103/PhysRevE.89.023302.
M. Ceriotti; J. More; D. E. Manolopoulos : i-PI: A Python interface for ab initio path integral molecular dynamics simulations; Computer Physics Communications. 2014. DOI : 10.1016/j.cpc.2013.10.027.
2013
B. G. Mckown; M. Ceriotti; C. C. Womack; E. Kamarchik; L. J. Butler et al. : Effects of High Angular Momentum on the Unimolecular Dissociation of CD2CD2OH: Theory and Comparisons with Experiment; Journal Of Physical Chemistry A. 2013. DOI : 10.1021/jp407913t.
G. Romanelli; M. Ceriotti; D. E. Manolopoulos; C. Pantalei; R. Senesi et al. : Direct Measurement of Competing Quantum Effects on the Kinetic Energy of Heavy Water upon Melting; The Journal of Physical Chemistry Letters. 2013. DOI : 10.1021/jz401538r.
M. Ceriotti; J. Cuny; M. Parrinello; D. E. Manolopoulos : Nuclear quantum effects and hydrogen bond fluctuations in water; Proceedings of the National Academy of Sciences. 2013. DOI : 10.1073/pnas.1308560110.
J. Liu; R. S. Andino; C. M. Miller; X. Chen; D. M. Wilkins et al. : A Surface-Specific Isotope Effect in Mixtures of Light and Heavy Water; The Journal of Physical Chemistry C. 2013. DOI : 10.1021/jp311986m.
M. Ceriotti; T. E. Markland : Efficient methods and practical guidelines for simulating isotope effects; The Journal of Chemical Physics. 2013. DOI : 10.1063/1.4772676.
M. Ceriotti; G. A. Tribello; M. Parrinello : Demonstrating the Transferability and the Descriptive Power of Sketch-Map; Journal of Chemical Theory and Computation. 2013. DOI : 10.1021/ct3010563.
2012
M. Ceriotti; G. A. R. Brain; O. Riordan; D. E. Manolopoulos : The inefficiency of re-weighted sampling and the curse of system size in high-order path integration; Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2012. DOI : 10.1098/rspa.2011.0413.
G. A. Tribello; M. Ceriotti; M. Parrinello : Using sketch-map coordinates to analyze and bias molecular dynamics simulations; Proceedings of the National Academy of Sciences. 2012. DOI : 10.1073/pnas.1201152109.
A. G. Seel; M. Ceriotti; P. P. Edwards; J. Mayers : Simultaneous measurement of lithium and fluorine momentum in; Journal of Physics: Condensed Matter. 2012. DOI : 10.1088/0953-8984/24/36/365401.
A. A. Hassanali; J. Cuny; M. Ceriotti; C. J. Pickard; M. Parrinello : The Fuzzy Quantum Proton in the Hydrogen Chloride Hydrates; Journal of the American Chemical Society. 2012. DOI : 10.1021/ja3014727.
M. Ceriotti; D. E. Manolopoulos : Efficient First-Principles Calculation of the Quantum Kinetic Energy and Momentum Distribution of Nuclei; Physical Review Letters. 2012. DOI : 10.1103/PhysRevLett.109.100604.
2011
J. A. Morrone; T. E. Markland; M. Ceriotti; B. J. Berne : Efficient multiple time scale molecular dynamics: Using colored noise thermostats to stabilize resonances; The Journal of Chemical Physics. 2011. DOI : 10.1063/1.3518369.
G. Miceli; M. Ceriotti; M. Bernasconi; M. Parrinello : Static disorder and structural correlations in the low-temperature phase of lithium imide; Physical Review B. 2011. DOI : 10.1103/PhysRevB.83.054119.
G. Miceli; M. Ceriotti; S. Angioletti-Uberti; M. Bernasconi; M. Parrinello : First-Principles Study of the High-Temperature Phase of Li; The Journal of Physical Chemistry C. 2011. DOI : 10.1021/jp200076p.
M. Ceriotti; G. A. Tribello; M. Parrinello : From the Cover: Simplifying the representation of complex free-energy landscapes using sketch-map; Proceedings of the National Academy of Sciences. 2011. DOI : 10.1073/pnas.1108486108.
M. Ceriotti; D. E. Manolopoulos; M. Parrinello : Accelerating the convergence of path integral dynamics with a generalized Langevin equation; The Journal of Chemical Physics. 2011. DOI : 10.1063/1.3556661.
2010
S. Angioletti-Uberti; M. Ceriotti; P. D. Lee; M. W. Finnis : Solid-liquid interface free energy through metadynamics simulations; Physical Review B. 2010. DOI : 10.1103/PhysRevB.81.125416.
M. Ceriotti; G. Bussi; M. Parrinello : Colored-Noise Thermostats à la Carte; Journal of Chemical Theory and Computation. 2010. DOI : 10.1021/ct900563s.
M. Ceriotti; G. Miceli; A. Pietropaolo; D. Colognesi; A. Nale et al. : Nuclear quantum effects in ab initio dynamics: Theory and experiments for lithium imide; Physical Review B. 2010. DOI : 10.1103/PhysRevB.82.174306.
M. Ceriotti; M. Parrinello : The -thermostat: selective normal-modes excitation by colored-noise Langevin dynamics; Procedia Computer Science. 2010. DOI : 10.1016/j.procs.2010.04.180.
M. Ceriotti; M. Parrinello; T. E. Markland; D. E. Manolopoulos : Efficient stochastic thermostatting of path integral molecular dynamics; The Journal of Chemical Physics. 2010. DOI : 10.1063/1.3489925.
G. A. Tribello; M. Ceriotti; M. Parrinello : A self-learning algorithm for biased molecular dynamics; Proceedings of the National Academy of Sciences. 2010. DOI : 10.1073/pnas.1011511107.
2009
M. Ceriotti; G. Bussi; M. Parrinello : Nuclear Quantum Effects in Solids Using a Colored-Noise Thermostat; Physical Review Letters. 2009. DOI : 10.1103/PhysRevLett.103.030603.
M. Ceriotti; G. Bussi; M. Parrinello : Langevin Equation with Colored Noise for Constant-Temperature Molecular Dynamics Simulations; Physical Review Letters. 2009. DOI : 10.1103/PhysRevLett.102.020601.
M. Ceriotti; S. Cereda; F. Montalenti; L. Miglio; M. Bernasconi : Ab initio study of the diffusion and decomposition pathways of SiHx species on Si(100); Physical Review B. 2009. DOI : 10.1103/PhysRevB.79.165437.
2008
M. Ceriotti; T. D. KüHne; M. Parrinello : An efficient and accurate decomposition of the Fermi operator; The Journal of Chemical Physics. 2008. DOI : 10.1063/1.2949515.
F. Zipoli; S. Cereda; M. Ceriotti; M. Bernasconi; L. Miglio et al. : First principles study of Ge∕Si exchange mechanisms at the Si(001) surface; Applied Physics Letters. 2008. DOI : 10.1063/1.2926683.
2007
S. Cereda; M. Ceriotti; F. Montalenti; M. Bernasconi; L. Miglio : Quantitative estimate of H abstraction by thermal SiH3 on hydrogenated Si(001)(2×1); Physical Review B. 2007. DOI : 10.1103/PhysRevB.75.235311.
M. Ceriotti; M. Bernasconi : Diffusion and desorption of SiH3 on hydrogenated H:Si(100)-(2×1) from first principles; Physical Review B. 2007. DOI : 10.1103/PhysRevB.76.245309.
M. Ceriotti; G. Bussi; M. Parrinello : Conjugate gradient heat bath for ill-conditioned actions; Physical Review E. 2007. DOI : 10.1103/PhysRevE.76.026707.
2006
M. Ceriotti; R. Ferrando; F. Montalenti : Impact-driven effects in thin-film growth: steering and transient mobility at the Ag(110) surface; Nanotechnology. 2006. DOI : 10.1088/0957-4484/17/14/033.
M. Ceriotti; F. Pietrucci; M. Bernasconi : Ab initio study of the vibrational properties of crystalline TeO2: The α, β, and γ phases; Physical Review B. 2006. DOI : 10.1103/PhysRevB.73.104304.
Teaching & PhD
Teaching
Materials Science and Engineering