Publications

Surface segregation in high-entropy alloys from alchemical machine learning

A. Mazitov; M. A. Springer; N. Lopanitsyna; G. Fraux; S. De et al. 

Journal Of Physics-Materials. 2024-04-01. Vol. 7, num. 2, p. 025007. DOI : 10.1088/2515-7639/ad2983.

Thermal transport of glasses via machine learning driven simulations

P. Pegolo; F. Grasselli 

Frontiers In Materials. 2024-03-06. Vol. 11, p. 1369034. DOI : 10.3389/fmats.2024.1369034.

Efficient and insightful descriptors for representing molecular and material space

A. J. Goscinski / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2024. 

Natural aging and vacancy trapping in Al-6xxx

A. C. P. Jain; M. Ceriotti; W. A. Curtin 

Journal Of Materials Research. 2023-12-21. DOI : 10.1557/s43578-023-01245-w.

Multiscale Modeling of Aqueous Electric Double Layers

M. Becker; P. R. Loche; M. Rezaei; A. Wolde-Kidan; Y. Uematsu et al. 

Chemical Reviews. 2023-12-20. Vol. 124, num. 1, p. 1-26. DOI : 10.1021/acs.chemrev.3c00307.

Accelerated chemical science with AI

S. Back; A. Aspuru-Guzik; M. Ceriotti; G. Gryn’ova; B. Grzybowski et al. 

Digital Discovery. 2023-12-06. Vol. 3, num. 1, p. 23-33. DOI : 10.1039/d3dd00213f.

Revealing the Formation Dynamics of Janus Polymer Particles: Insights from Experiments and Molecular Dynamics

M. Nedyalkova; G. Russo; P. R. Loche; M. Lattuada 

Journal Of Chemical Information And Modeling. 2023-11-30. Vol. 63, num. 23, p. 7453-7463. DOI : 10.1021/acs.jcim.3c01547.

Divide-and-conquer potentials enable scalable and accurate predictions of forces and energies in atomistic systems

C. Zeni; A. Anelli; A. Glielmo; S. de Gironcoli; K. Rossi 

Digital Discovery. 2023-11-15. Vol. 3, num. 1, p. 113-121. DOI : 10.1039/d3dd00155e.

Robustness of Local Predictions in Atomistic Machine Learning Models

S. Chong; F. Grasselli; C. Ben Mahmoud; J. D. Morrow; V. L. Deringer et al. 

Journal Of Chemical Theory And Computation. 2023-11-10. Vol. 19, num. 22, p. 8020-8031. DOI : 10.1021/acs.jctc.3c00704.

Accuracy Assessment of Atomistic Neural Network Potentials: The Impact of Cutoff Radius and Message Passing

J. Xia; Y. Zhang; B. Jiang 

Journal Of Physical Chemistry A. 2023-11-09. Vol. 127, num. 46, p. 9874-9883. DOI : 10.1021/acs.jpca.3c06024.

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

K. K. Huguenin-Dumittan; P. R. Loche; N. Haoran; M. Ceriotti 

Journal Of Physical Chemistry Letters. 2023-10-20. Vol. 14, num. 43, p. 9612-9618. DOI : 10.1021/acs.jpclett.3c02375.

Universal machine learning for the response of atomistic systems to external fields

Y. Zhang; B. Jiang 

Nature Communications. 2023-10-12. Vol. 14, num. 1, p. 6424. DOI : 10.1038/s41467-023-42148-y.

Self-interaction and transport of solvated electrons in molten salts

P. Pegolo; S. Baroni; F. Grasselli 

Journal Of Chemical Physics. 2023-09-07. Vol. 159, num. 9, p. 094116. DOI : 10.1063/5.0169474.

van der Waals Materials for Overcoming Fundamental Limitations in Photonic Integrated Circuitry

A. A. Vyshnevyy; G. A. Ermolaev; D. V. Grudinin; K. V. Voronin; I. Kharichkin et al. 

Nano Letters. 2023-08-24. Vol. 23, num. 17, p. 8057-8064. DOI : 10.1021/acs.nanolett.3c02051.

Fast evaluation of spherical harmonics with sphericart

F. Bigi; G. Fraux; N. J. Browning; M. Ceriotti 

Journal Of Chemical Physics. 2023-08-14. Vol. 159, num. 6, p. 064802. DOI : 10.1063/5.0156307.

Effect of a temperature gradient on the screening properties of ionic fluids

A. Grisafi; F. Grasselli 

Physical Review Materials. 2023-04-27. Vol. 7, num. 4, p. 045803. DOI : 10.1103/PhysRevMaterials.7.045803.

Modeling high-entropy transition metal alloys with alchemical compression

N. Lopanitsyna; G. Fraux; M. A. Springer; S. De; M. Ceriotti 

Physical Review Materials. 2023-04-26. Vol. 7, num. 4, p. 045802. DOI : 10.1103/PhysRevMaterials.7.045802.

A data-driven interpretation of the stability of organic molecular crystals

R. K. Cersonsky; M. Pakhnova; E. A. Engel; M. Ceriotti 

Chemical Science. 2023-01-16. Vol. 14, num. 5, p. 1272-1285. DOI : 10.1039/d2sc06198h.

Modelling of metal alloys in realistic conditions with machine learning

N. Lopanitsyna / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2023. 

Machine-learning the electronic density of states: electronic properties without quantum mechanics

C. Ben Mahmoud / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2023. 

Electrokinetic, electrochemical, and electrostatic surface potentials of the pristine water liquid-vapor interface

M. R. Becker; P. Loche; R. R. Netz 

Journal Of Chemical Physics. 2022-12-28. Vol. 157, num. 24, p. 240902. DOI : 10.1063/5.0127869.

A smooth basis for atomistic machine learning

F. Bigi; K. K. Huguenin-Dumittan; M. Ceriotti; D. E. Manolopoulos 

Journal Of Chemical Physics. 2022-12-21. Vol. 157, num. 23, p. 234101. DOI : 10.1063/5.0124363.

Beyond potentials: Integrated machine learning models for materials

M. Ceriotti 

Mrs Bulletin. 2022-12-06. Vol. 47, p. 1045–1053. DOI : 10.1557/s43577-022-00440-0.

Incompleteness of graph neural networks for points clouds in three dimensions

S. N. Pozdnyakov; M. Ceriotti 

Machine Learning-Science And Technology. 2022-12-01. Vol. 3, num. 4, p. 045020. DOI : 10.1088/2632-2153/aca1f8.

Comment on “Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions” [J. Chem. Phys. 156, 034302 (2022)]

S. N. N. Pozdnyakov; M. J. J. Willatt; A. P. P. Bartok; C. Ortner; G. Csanyi et al. 

Journal Of Chemical Physics. 2022-11-07. Vol. 157, num. 17, p. 177101. DOI : 10.1063/5.0088404.

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

L. Gigli; M. Veit; M. Kotiuga; G. Pizzi; N. Marzari et al. 

Npj Computational Materials. 2022-09-29. Vol. 8, num. 1, p. 209. DOI : 10.1038/s41524-022-00845-0.

Predicting hot-electron free energies from ground-state data

C. Ben Mahmoud; F. Grasselli; M. Ceriotti 

Physical Review B. 2022-09-27. Vol. 106, num. 12, p. L121116. DOI : 10.1103/PhysRevB.106.L121116.

A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids

M. Cordova; E. A. Engel; A. Stefaniuk; F. Paruzzo; A. Hofstetter et al. 

Journal Of Physical Chemistry C. 2022-09-22. Vol. 126, num. 39, p. 16710–16720. DOI : 10.1021/acs.jpcc.2c03854.

Effects of surface rigidity and metallicity on dielectric properties and ion interactions at aqueous hydrophobic interfaces

P. Loche; L. Scalfi; M. A. Amu; O. Schullian; D. J. Bonthuis et al. 

Journal Of Chemical Physics. 2022-09-07. Vol. 157, num. 9, p. 094707. DOI : 10.1063/5.0101509.

Topology, Oxidation States, and Charge Transport in Ionic Conductors

P. Pegolo; S. Baroni; F. Grasselli 

Annalen Der Physik. 2022-08-17.  p. 2200123. DOI : 10.1002/andp.202200123.

Roadmap on Machine learning in electronic structure

H. J. Kulik; T. Hammerschmidt; J. Schmidt; S. Botti; M. A. L. Marques et al. 

Electronic Structure. 2022-06-01. Vol. 4, num. 2, p. 023004. DOI : 10.1088/2516-1075/ac572f.

Unified theory of atom-centered representations and message-passing machine-learning schemes

J. Nigam; S. Pozdnyakov; G. Fraux; M. Ceriotti 

Journal Of Chemical Physics. 2022-05-28. Vol. 156, num. 20, p. 204115. DOI : 10.1063/5.0087042.

Molecular dynamics simulations of the evaporation of hydrated ions from aqueous solution

P. Loche; D. J. Bonthuis; R. R. Netz 

Communications Chemistry. 2022-04-19. Vol. 5, num. 1, p. 55. DOI : 10.1038/s42004-022-00669-5.

Investigating finite-size effects in molecular dynamics simulations of ion diffusion, heat transport, and thermal motion in superionic materials

F. Grasselli 

Journal Of Chemical Physics. 2022-04-07. Vol. 156, num. 13, p. 134705. DOI : 10.1063/5.0087382.

A complete description of thermodynamic stabilities of molecular crystals

V. Kapil; E. A. Engel 

Proceedings Of The National Academy Of Sciences Of The United States Of America. 2022-02-08. Vol. 119, num. 6, p. e2111769119. DOI : 10.1073/pnas.2111769119.

Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

P. Pegolo; S. Baroni; F. Grasselli 

Npj Computational Materials. 2022-01-28. Vol. 8, num. 1, p. 24. DOI : 10.1038/s41524-021-00693-4.

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

J. Nigam; M. J. Willatt; M. Ceriotti 

Journal Of Chemical Physics. 2022-01-07. Vol. 156, num. 1, p. 014115. DOI : 10.1063/5.0072784.

Ranking the synthesizability of hypothetical zeolites with the sorting hat

B. A. Helfrecht; G. Pireddu; R. Semino; S. Auerbach; M. Ceriotti 

Digital Discovery. 2022. Vol. 1, num. 6, p. 779-789. DOI : 10.1039/D2DD00056C.

Characterization and prediction of peptide structures on inorganic surfaces

D. Maksimov / M. Ceriotti; M. Rossi Carvalho (Dir.)  

Lausanne, EPFL, 2022. 

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

R. Fabregat; A. Fabrizio; E. A. Engel; B. Meyer; V. Juraskova et al. 

Journal of Chemical Theory and Computation. 2022. Vol. 18, num. 3, p. 1467-1479. DOI : 10.1021/acs.jctc.1c00813.

A route to hierarchical assembly of colloidal diamond

Y. Zhou; R. K. Cersonsky; S. C. Glotzer 

Soft Matter. 2022. Vol. 18, num. 2, p. 304-311. DOI : 10.1039/d1sm01418h.

2020 JCP Emerging Investigator Special Collection

M. Ceriotti; L. Jensen; D. E. Manolopoulos; T. J. Martinez; A. Michaelides et al. 

Journal Of Chemical Physics. 2021-12-21. Vol. 155, num. 23, p. 230401. DOI : 10.1063/5.0078934.

Reply to: On the liquid-liquid phase transition of dense hydrogen

B. Cheng; G. Mazzola; C. J. Pickard; M. Ceriotti 

Nature. 2021-12-16. Vol. 600, num. 7889, p. E15-E16. DOI : 10.1038/s41586-021-04079-w.

Learning Electron Densities in the Condensed Phase

A. M. Lewis; A. Grisafi; M. Ceriotti; M. Rossi 

Journal Of Chemical Theory And Computation. 2021-11-09. Vol. 17, num. 11, p. 7203-7214. DOI : 10.1021/acs.jctc.1c00576.

Bayesian probabilistic assignment of chemical shifts in organic solids

M. Cordova; M. Balodis; B. S. de Almeida; M. Ceriotti; L. Emsley 

Science Advances. 2021-11-01. Vol. 7, num. 48, p. eabk2341. DOI : 10.1126/sciadv.abk2341.

Optimal radial basis for density-based atomic representations

A. Goscinski; F. Musil; S. Pozdnyakov; J. Nigam; M. Ceriotti 

Journal Of Chemical Physics. 2021-09-14. Vol. 155, num. 10, p. e104106. DOI : 10.1063/5.0057229.

Improving sample and feature selection with principal covariates regression

R. K. Cersonsky; B. A. Helfrecht; E. A. Engel; S. Kliavinek; M. Ceriotti 

Machine Learning-Science And Technology. 2021-09-01. Vol. 2, num. 3, p. 035038. DOI : 10.1088/2632-2153/abfe7c.

Introduction: Machine Learning at the Atomic Scale

M. Ceriotti; C. Clementi; O. A. von Lilienfeld 

Chemical Reviews. 2021-08-25. Vol. 121, num. 16, p. 9719-9721. DOI : 10.1021/acs.chemrev.1c00598.

Gaussian Process Regression for Materials and Molecules

V. L. Deringer; A. P. Bartok; N. Bernstein; D. M. Wilkins; M. Ceriotti et al. 

Chemical Reviews. 2021-08-25. Vol. 121, num. 16, p. 10073-10141. DOI : 10.1021/acs.chemrev.1c00022.

Physics-Inspired Structural Representations for Molecules and Materials

F. Musil; A. Grisafi; A. P. Bartok; C. Ortner; G. Csanyi et al. 

Chemical Reviews. 2021-08-25. Vol. 121, num. 16, p. 9759-9815. DOI : 10.1021/acs.chemrev.1c00021.

Importance of Nuclear Quantum Effects for NMR Crystallography

E. A. Engel; V. Kapil; M. Ceriotti 

Journal Of Physical Chemistry Letters. 2021-08-19. Vol. 12, num. 32, p. 7701-7707. DOI : 10.1021/acs.jpclett.1c01987.

Invariance principles in the theory and computation of transport coefficients

F. Grasselli; S. Baroni 

The European Physical Journal. 2021-08-03. Vol. B94, num. 8, p. 160. DOI : 10.1140/epjb/s10051-021-00152-5.

Quantum vibronic effects on the electronic properties of solid and molecular carbon

A. Kundu; M. Govoni; H. Yang; M. Ceriotti; F. Gygi et al. 

Physical Review Materials. 2021-07-26. Vol. 5, num. 7, p. L070801. DOI : 10.1103/PhysRevMaterials.5.L070801.

Chemical physics software

C. D. Sherrill; D. E. Manolopoulos; T. J. Martinez; M. Ceriotti; A. Michaelides 

Journal Of Chemical Physics. 2021-07-07. Vol. 155, num. 1, p. 010401. DOI : 10.1063/5.0059886.

Modeling the Ga/As binary system across temperatures and compositions from first principles

G. Imbalzano; M. Ceriotti 

Physical Review Materials. 2021-06-22. Vol. 5, num. 6, p. 063804. DOI : 10.1103/PhysRevMaterials.5.063804.

Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps

F. Giberti; G. A. Tribello; M. Ceriotti 

Journal Of Chemical Theory And Computation. 2021-06-08. Vol. 17, num. 6, p. 3292-3308. DOI : 10.1021/acs.jctc.0c01177.

The role of feature space in atomistic learning

A. Goscinski; G. Fraux; G. Imbalzano; M. Ceriotti 

Machine Learning-Science And Technology. 2021-06-01. Vol. 2, num. 2, p. 025028. DOI : 10.1088/2632-2153/abdaf7.

Machine learning for metallurgy III: A neural network potential for Al-Mg-Si

A. C. P. Jain; D. Marchand; A. Glensk; M. Ceriotti; W. A. Curtin 

Physical Review Materials. 2021-05-26. Vol. 5, num. 5, p. 053805. DOI : 10.1103/PhysRevMaterials.5.053805.

Machine learning meets chemical physics

M. Ceriotti; C. Clementi; O. Anatole von Lilienfeld 

Journal Of Chemical Physics. 2021-04-28. Vol. 154, num. 16, p. 160401. DOI : 10.1063/5.0051418.

Origins of structural and electronic transitions in disordered silicon

V. L. Deringer; N. Bernstein; G. Csányi; C. Ben Mahmoud; M. Ceriotti et al. 

Nature. 2021-01-06. Vol. 589, num. 7840, p. 59-64. DOI : 10.1038/s41586-020-03072-z.

Local invertibility and sensitivity of atomic structure-feature mappings

S. N. Pozdnyakov; L. Zhang; C. Ortner; G. Csányi; M. Ceriotti 

Open Research Europe. 2021. Vol. 1, p. 1-22, 126. DOI : 10.12688/openreseurope.14156.1.

Transferable machine-learning models of complex materials: the case of GaAs

G. Imbalzano / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2021. 

Structure-Property Relationships in Complex Materials by Combining Supervised and Unsupervised Machine Learning

B. A. Helfrecht / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2021. 

Physics-enhanced machine learning with symmetry-adapted and long-range representations

A. Grisafi / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2021. 

Finite-temperature materials modeling from the quantum nuclei to the hot electron regime

N. Lopanitsyna; C. Ben Mahmoud; M. Ceriotti 

Physical Review Materials. 2021. Vol. 5, num. 4, p. 043802. DOI : 10.1103/PhysRevMaterials.5.043802.

A general and efficient framework for atomistic machine learning

F. B. C. Musil / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2021. 

Uncertainty estimation for molecular dynamics and sampling

G. Imbalzano; Y. Zhuang; V. Kapil; K. Rossi; E. A. Engel et al. 

The Journal of Chemical Physics. 2021. Vol. 154, num. 7, p. 074102. DOI : 10.1063/5.0036522.

Efficient implementation of atom-density representations

F. Musil; M. Veit; A. Goscinski; G. Fraux; M. J. Willatt et al. 

The Journal of Chemical Physics. 2021. Vol. 154, num. 11, p. 114109. DOI : 10.1063/5.0044689.

Simulating the ghost: quantum dynamics of the solvated electron

J. Lan; V. Kapil; P. Gasparotto; M. Ceriotti; M. Iannuzzi et al. 

Nature Communications. 2021. Vol. 12, num. 1, p. 766. DOI : 10.1038/s41467-021-20914-0.

Multi-scale approach for the prediction of atomic scale properties

A. Grisafi; J. Nigam; M. Ceriotti 

Chemical Science. 2021. Vol. 12, num. 6, p. 2078-2090. DOI : 10.1039/D0SC04934D.

Learning the electronic density of states in condensed matter

C. Ben Mahmoud; A. Anelli; G. Csanyi; M. Ceriotti 

Physical Review B. 2020-12-14. Vol. 102, num. 23, p. 235130. DOI : 10.1103/PhysRevB.102.235130.

Oxidation States, Thouless’ Pumps, and Nontrivial Ionic Transport in Nonstoichiometric Electrolytes

P. Pegolo; F. Grasselli; S. Baroni 

Physical Review X. 2020-11-12. Vol. 10, num. 4, p. 041031. DOI : 10.1103/PhysRevX.10.041031.

Incompleteness of Atomic Structure Representations

S. N. Pozdnyakov; M. J. Willatt; A. P. Bartok; C. Ortner; G. Csanyi et al. 

Physical Review Letters. 2020-10-12. Vol. 125, num. 16, p. 166001. DOI : 10.1103/PhysRevLett.125.166001.

Gas-sieving zeolitic membranes fabricated by condensation of precursor nanosheets

M. Dakhchoune; L. F. Villalobos; R. Semino; L. Liu; M. Rezaei et al. 

Nature Materials. 2020-10-05. Vol. 20, num. 3, p. 362-369. DOI : 10.1038/s41563-020-00822-2.

DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening

J. Fine; M. Muhoberac; G. Fraux; G. Chopra 

Journal Of Chemical Information And Modeling. 2020-09-28. Vol. 60, num. 9, p. 4137-4143. DOI : 10.1021/acs.jcim.0c00122.

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

P. Gkeka; G. Stoltz; A. B. Farimani; Z. Belkacemi; M. Ceriotti et al. 

Journal Of Chemical Theory And Computation. 2020-08-11. Vol. 16, num. 8, p. 4757-4775. DOI : 10.1021/acs.jctc.0c00355.

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

K. Rossi; V. Juraskova; R. Wischert; L. Garel; C. Corminboeuf et al. 

Journal Of Chemical Theory And Computation. 2020-08-11. Vol. 16, num. 8, p. 5139-5149. DOI : 10.1021/acs.jctc.0c00362.

3D Ordering at the Liquid-Solid Polar Interface of Nanowires

M. Zamani; G. Imbalzano; N. Tappy; D. T. L. Alexander; S. Marti-Sanchez et al. 

Advanced Materials. 2020-08-06. Vol. 32, num. 38, p. 2001030. DOI : 10.1002/adma.202001030.

Heat and charge transport in H2O at ice-giant conditions from ab initio molecular dynamics simulations

F. Grasselli; L. Stixrude; S. Baroni 

Nature Communications. 2020-07-17. Vol. 11, num. 1, p. 3605. DOI : 10.1038/s41467-020-17275-5.

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

M. Veit; D. M. Wilkins; Y. Yang; R. A. DiStasio; M. Ceriotti 

Journal Of Chemical Physics. 2020-07-14. Vol. 153, num. 2, p. 024113. DOI : 10.1063/5.0009106.

Quantum kinetic energy and isotope fractionation in aqueous ionic solutions

L. Wang; M. Ceriotti; T. E. Markland 

Physical Chemistry Chemical Physics. 2020-05-21. Vol. 22, num. 19, p. 10490-10499. DOI : 10.1039/c9cp06483d.

Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats

V. Kapil; D. M. Wilkins; J. Lan; M. Ceriotti 

Journal Of Chemical Physics. 2020-03-31. Vol. 152, num. 12, p. 124104. DOI : 10.1063/1.5141950.

Structural Screening and Design of Platinum Nanosamples for Oxygen Reduction

K. Rossi; G. G. Asara; F. Baletto 

Acs Catalysis. 2020-03-20. Vol. 10, num. 6, p. 3911-3920. DOI : 10.1021/acscatal.9b05202.

Understanding How Ligand Functionalization Influences CO2 and N-2 Adsorption in a Sodalite Metal-Organic Framework

M. Asgari; R. Semino; P. A. Schouwink; I. Kochetygov; J. Tarver et al. 

Chemistry Of Materials. 2020-02-25. Vol. 32, num. 4, p. 1526-1536. DOI : 10.1021/acs.chemmater.9b04631.

Accurate Description of Nuclear Quantum Effects with High-Order Perturbed Path Integrals (HOPPI)

I. Poltaysky; V. Kapil; M. Ceriotti; K. S. Kim; A. Tkatchenko 

Journal Of Chemical Theory And Computation. 2020-02-01. Vol. 16, num. 2, p. 1128-1135. DOI : 10.1021/acs.jctc.9b00881.

Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification

B. Cheng; M. Ceriotti; G. A. Tribello 

Journal Of Chemical Physics. 2020-01-31. Vol. 152, num. 4, p. 044103. DOI : 10.1063/1.5134461.

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

P. Gasparotto; D. Bochicchio; M. Ceriotti; G. M. Pavan 

Journal Of Physical Chemistry B. 2020-01-23. Vol. 124, num. 3, p. 589-599. DOI : 10.1021/acs.jpcb.9b11015.

Structure-property maps with Kernel principal covariates regression

B. A. Helfrecht; R. K. Cersonsky; G. Fraux; M. Ceriotti 

Machine Learning: Science and Technology. 2020. Vol. 1, num. 4, p. 045021. DOI : 10.1088/2632-2153/aba9ef.

Characterising Structure and Stability of Materials using Machine Learning

A. Anelli / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2020. 

Evidence for supercritical behaviour of high-pressure liquid hydrogen

B. Cheng; G. Mazzola; C. J. Pickard; M. Ceriotti 

Nature. 2020. Vol. 585, num. 7824, p. 217-220. DOI : 10.1038/s41586-020-2677-y.

Chemiscope: interactive structure-property explorer for materials and molecules

G. Fraux; R. Cersonsky; M. Ceriotti 

Journal of Open Source Software. 2020. Vol. 5, num. 51, p. 2117. DOI : 10.21105/joss.02117.

Recursive evaluation and iterative contraction of N-body equivariant features

J. Nigam; S. Pozdnyakov; M. Ceriotti 

The Journal of Chemical Physics. 2020. Vol. 153, num. 12, p. 121101. DOI : 10.1063/5.0021116.

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

G. Csányi; M. J. Willatt; M. Ceriotti 

Machine Learning Meets Quantum Physics; Springer International Publishing, 2020.

Nuclear Quantum Effects: Fast and Accurate

V. Kapil / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2020. 

Machine Learning-Guided Approach for Studying Solvation Environments

Y. Basdogan; M. C. Groenenboom; E. Henderson; S. De; S. B. Rempe et al. 

Journal Of Chemical Theory And Computation. 2020-01-01. Vol. 16, num. 1, p. 633-642. DOI : 10.1021/acs.jctc.9b00605.

Iterative Unbiasing of Quasi-Equilibrium Sampling

F. Giberti; B. Cheng; G. A. Tribello; M. Ceriotti 

Journal Of Chemical Theory And Computation. 2020-01-01. Vol. 16, num. 1, p. 100-107. DOI : 10.1021/acs.jctc.9b00907.

Atomistic modeling of the solid-liquid interface of metals and alloys

E. Baldi / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2020. 

Representations and descriptors unifying the study of molecular and bulk systems

K. Rossi; J. Cumby 

International Journal Of Quantum Chemistry. 2019-12-27.  p. e26151. DOI : 10.1002/qua.26151.

Determination and evaluation of the nonadditivity in wetting of molecularly heterogeneous surfaces

Z. Luo; A. Murello; D. M. Wilkins; F. Kovacik; J. Kohlbrecher et al. 

Proceedings of the National Academy of Sciences. 2019-12-17. Vol. 116, num. 51, p. 25516-25523. DOI : 10.1073/pnas.1916180116.

Phonon Lifetimes Throughout the Brillouin Zone at Elevated Temperatures from Experiment and ab initio

A. Glensk; B. Grabowski; T. Hickel; J. Neugebauer; J. Neuhaus et al. 

Physical Review Letters. 2019-12-02. Vol. 123, num. 23, p. 235501. DOI : 10.1103/PhysRevLett.123.235501.

Machine Learning at the Atomic Scale

F. Musil; M. Ceriotti 

Chimia. 2019-12-01. Vol. 73, num. 12, p. 972-982. DOI : 10.2533/chimia.2019.972.

Multi-Scale Electrolyte Transport Simulations for Lithium Ion Batteries

F. Hanke; N. Modrow; R. L. C. Akkermans; I. Korotkin; F. C. Mocanu et al. 

Journal Of The Electrochemical Society. 2019-11-22. Vol. 167, num. 1, p. 013522. DOI : 10.1149/2.0222001JES.

Electron density learning of non-covalent systems

A. Fabrizio; A. Grisafi; B. Meyer; M. Ceriotti; C. Corminboeuf 

Chemical Science. 2019-11-07. Vol. 10, num. 41, p. 9424-9432. DOI : 10.1039/c9sc02696g.

Thermal Engineering of Metal-Organic Frameworks for Adsorption Applications: A Molecular Simulation Perspective

J. Wieme; S. Vandenbrande; A. Lamaire; V. Kapil; L. Vanduyfhuys et al. 

Acs Applied Materials & Interfaces. 2019-10-23. Vol. 11, num. 42, p. 38697-38707. DOI : 10.1021/acsami.9b12533.

Correlating Oxygen Reduction Reaction Activity and Structural Rearrangements in MgO-Supported Platinum Nanoparticles

K. Rossi; G. G. Asara; F. Baletto 

Chemphyschem. 2019-09-03. Vol. 20, num. 22, p. 3037-3044. DOI : 10.1002/cphc.201900564.

Learning (from/about) the ground-state electron density

C. Corminboeuf; M. Ceriotti; A. Fabrizio; A. Grisafi; B. Meyer et al. 

2019-08-25. Fall National Meeting and Exposition of the American-Chemical-Society (ACS), San Diego, CA, Aug 25-29, 2019.

Quantum mechanical static dipole polarizabilities in the QM7b and AlphaML showcase databases

Y. Yang; K. U. Lao; D. M. Wilkins; A. Grisafi; M. Ceriotti et al. 

Scientific Data. 2019-08-19. Vol. 6, p. 152. DOI : 10.1038/s41597-019-0157-8.

Path-integral dynamics of water using curvilinear centroids

G. Trenins; M. J. Willatt; S. C. Althorpe 

Journal of Chemical Physics. 2019-08-07. Vol. 151, num. 5, p. 054109. DOI : 10.1063/1.5100587.

Energy Relaxation and Thermal Diffusion in Infrared Pump-Probe Spectroscopy of Hydrogen-Bonded Liquids

R. Dettori; M. Ceriotti; J. Hunger; L. Colombo; D. Donadio 

Journal Of Physical Chemistry Letters. 2019-06-20. Vol. 10, num. 12, p. 3447-3452. DOI : 10.1021/acs.jpclett.9b01272.

Modeling Superlattices of Dipolar and Polarizable Semiconducting Nanoparticles

S. Mazzotti; F. Giberti; G. Galli 

Nano Letters. 2019-06-01. Vol. 19, num. 6, p. 3912-3917. DOI : 10.1021/acs.nanolett.9b01142.

Chemical machine learning with kernels: The impact of loss functions

Quang Van Nguyen; S. De; J. Lin; V. Cevher 

International Journal Of Quantum Chemistry. 2019-05-05. Vol. 119, num. 9, p. e25872. DOI : 10.1002/qua.25872.

Data Science Based Mg Corrosion Engineering

T. Wuerger; C. Feiler; F. Musil; G. B. V. Feldbauer; D. Hoeche et al. 

Frontiers In Materials. 2019-04-05. Vol. 6, p. 53. DOI : 10.3389/fmats.2019.00053.

Equation of State of Fluid Methane from First Principles with Machine Learning Potentials

M. Veit; S. K. Jain; S. Bonakala; I. Rudra; D. Hohl et al. 

Journal Of Chemical Theory And Computation. 2019-04-01. Vol. 15, num. 4, p. 2574-2586. DOI : 10.1021/acs.jctc.8b01242.

Physics-based machine learning for materials and molecules

M. Ceriotti; E. Engel; M. Willatt 

2019-03-31. National Meeting of the American-Chemical-Society (ACS), Orlando, FL, Mar 31-Apr 04, 2019.

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

V. Kapil; M. Rossi; O. Marsalek; R. Petraglia; Y. Litman et al. 

Computer Physics Communications. 2019-03-01. Vol. 236, p. 214-223. DOI : 10.1016/j.cpc.2018.09.020.

Transferable Machine-Learning Model of the Electron Density

A. Grisafi; A. Fabrizio; B. Meyer; D. M. Wilkins; C. Corminboeuf et al. 

Acs Central Science. 2019-01-23. Vol. 5, num. 1, p. 57-64. DOI : 10.1021/acscentsci.8b00551.

Ab initio thermodynamics of liquid and solid water

B. Cheng; E. A. Engel; J. Behler; C. Dellago; M. Ceriotti 

Proceedings Of The National Academy Of Sciences Of The United States Of America. 2019-01-22. Vol. 116, num. 4, p. 1110-1115. DOI : 10.1073/pnas.1815117116.

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

A. Grisafi; D. M. Wilkins; M. J. Willatt; M. Ceriotti 

Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions; Cornell University, 2019. p. 1-21.

Incorporating long-range physics in atomic-scale machine learning

A. Grisafi; M. Ceriotti 

The Journal of Chemical Physics. 2019. Vol. 151, num. 20, p. 204105. DOI : 10.1063/1.5128375.

Assessment of Approximate Methods for Anharmonic Free Energies

V. Kapil; E. A. Engel; M. Rossi; M. Ceriotti 

Journal of Chemical Theory and Computation. 2019. Vol. 15, num. 11, p. 5845-5857. DOI : 10.1021/acs.jctc.9b00596.

A Bayesian approach to NMR crystal structure determination

E. A. Engel; A. Anelli; A. Hofstetter; F. M. Paruzzo; L. Emsley et al. 

Physical Chemistry Chemical Physics. 2019. Vol. 21, num. 42, p. 23385-23400. DOI : 10.1039/C9CP04489B.

A new kind of atlas of zeolite building blocks

B. A. Helfrecht; R. Semino Barbaresi; G. Pireddu; S. M. Auerbach; M. Ceriotti 

The Journal of Chemical Physics. 2019. Vol. 151, num. 15, p. 154112. DOI : 10.1063/1.5119751.

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

B. A. Helfrecht; P. Gasparotto; F. Giberti; M. Ceriotti 

Frontiers in Molecular Biosciences. 2019. Vol. 6, p. 24. DOI : 10.3389/fmolb.2019.00024.

Barely porous organic cages for hydrogen isotope separation

M. Liu; L. Zhang; M. A. Little; V. Kapil; M. Ceriotti et al. 

Science. 2019. Vol. 366, num. 6465, p. 613-620. DOI : 10.1126/science.aax7427.

Unsupervised machine learning in atomistic simulations, between predictions and understanding

M. Ceriotti 

The Journal of Chemical Physics. 2019. Vol. 150, num. 15, p. 150901. DOI : 10.1063/1.5091842.

An In-Situ Neutron Diffraction and DFT Study of Hydrogen Adsorption in a Sodalite-Type Metal-Organic Framework, Cu-BTTri

M. Asgari; R. Semino; P. Schouwink; I. Kochetygov; O. Trukhina et al. 

European Journal of Inorganic Chemistry. 2019. Vol. 2019, num. 8, p. 1147-1154. DOI : 10.1002/ejic.201801253.

Atom-density representations for machine learning

M. J. Willatt; F. Musil; M. Ceriotti 

The Journal of Chemical Physics. 2019. Vol. 150, num. 15, p. 154110. DOI : 10.1063/1.5090481.

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

M. J. Willatt; F. Musil; M. Ceriotti; M. A. Langovoy 

Journal of Chemical Theory and Computation. 2019. Vol. 15, num. 2, p. 906-915. DOI : 10.1021/acs.jctc.8b00959.

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

N. Raimbault; A. Grisafi; M. Ceriotti; M. Rossi 

New Journal of Physics. 2019. Vol. 21, num. 10, p. 105001. DOI : 10.1088/1367-2630/ab4509.

Modeling the Structural and Thermal Properties of Loaded Metal–Organic Frameworks. An Interplay of Quantum and Anharmonic Fluctuations

V. Kapil; J. Wieme; S. Vandenbrande; A. Lamaire; V. Van Speybroeck et al. 

Journal of Chemical Theory and Computation. 2019. Vol. 15, num. 5, p. 3237-3249. DOI : 10.1021/acs.jctc.8b01297.

Accurate molecular polarizabilities with coupled cluster theory and machine learning

D. M. Wilkins; A. Grisafi; Y. Yang; K. U. Lao; R. A. DiStasio et al. 

Proceedings of the National Academy of Sciences. 2019. Vol. 116, num. 9, p. 3401-3406. DOI : 10.1073/pnas.1816132116.

Predicting homogeneous nucleation rate from atomistic simulations

B. Cheng / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2019. 

Theoretical prediction of the homogeneous ice nucleation rate: disentangling thermodynamics and kinetics

B. Cheng; C. Dellago; M. Ceriotti 

Physical Chemistry Chemical Physics. 2018-12-07. Vol. 20, num. 45, p. 28732-28740. DOI : 10.1039/c8cp04561e.

Comment on “Water- water correlations in electrolyte solutions probed by hyper-Rayleigh scattering” [J. Chem. Phys. 147, 214505 (2017)]

Y. Chen; H. I. Okur; N. Dupertuis; J. Dedic; D. M. Wilkins et al. 

Journal Of Chemical Physics. 2018-10-28. Vol. 149, num. 16, p. 167101. DOI : 10.1063/1.5023579.

Data-driven many-body representations with chemical accuracy for molecular simulations from the gas to the condensed phase

Thuong Nguyen; E. Szekely; G. Imbalzano; J. Behler; G. Csanyi et al. 

2018-08-19. 256th National Meeting and Exposition of the American-Chemical-Society (ACS) – Nanoscience, Nanotechnology and Beyond, Boston, MA, Aug 19-23, 2018.

Machine learning across the periodic table

M. Willatt 

2018-08-19. 256th National Meeting and Exposition of the American-Chemical-Society (ACS) – Nanoscience, Nanotechnology and Beyond, Boston, MA, Aug 19-23, 2018.

Analyzing Fluxional Molecules Using DORI

L. Vannay; B. Meyer; R. Petraglia; G. Sforazzini; M. Ceriotti et al. 

Journal of Chemical Theory and Computation. 2018-03-24. Vol. 14, num. 5, p. 2370-2379. DOI : 10.1021/acs.jctc.7b01176.

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

M. Hellström; M. Ceriotti; J. Behler 

The Journal of Physical Chemistry B. 2018. Vol. 122, num. 44, p. 10158-10171. DOI : 10.1021/acs.jpcb.8b06433.

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

J. Yang; S. De; J. E. Campbell; S. Li; M. Ceriotti et al. 

Chemistry of Materials. 2018. Vol. 30, num. 13, p. 4361-4371. DOI : 10.1021/acs.chemmater.8b01621.

Chemical shifts in molecular solids by machine learning

F. M. Paruzzo; A. Hofstetter; F. Musil; S. De; M. Ceriotti et al. 

Nature Communications. 2018. Vol. 9, num. 1, p. 4501. DOI : 10.1038/s41467-018-06972-x.

Generalized convex hull construction for materials discovery

A. Anelli; E. A. Engel; C. J. Pickard; M. Ceriotti 

Physical Review Materials. 2018. Vol. 2, num. 10, p. 103804. DOI : 10.1103/PhysRevMaterials.2.103804.

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

T. T. Nguyen; E. Székely; G. Imbalzano; J. Behler; G. Csányi et al. 

The Journal of Chemical Physics. 2018. Vol. 148, num. 24, p. 241725. DOI : 10.1063/1.5024577.

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

Y. Litman; D. Donadio; M. Ceriotti; M. Rossi 

The Journal of Chemical Physics. 2018. Vol. 148, num. 10, p. 102320. DOI : 10.1063/1.5002537.

Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

M. J. Willatt; F. Musil; M. Ceriotti 

Physical Chemistry Chemical Physics. 2018. Vol. 20, num. 47, p. 29661-29668. DOI : 10.1039/C8CP05921G.

Approximating Matsubara dynamics using the planetary model: Tests on liquid water and ice

M. J. Willatt; M. Ceriotti; S. C. Althorpe 

The Journal of Chemical Physics. 2018. Vol. 148, num. 10, p. 102336. DOI : 10.1063/1.5004808.

Hydrogen Diffusion and Trapping in α-Iron: The Role of Quantum and Anharmonic Fluctuations

B. Cheng; A. T. Paxton; M. Ceriotti 

Physical Review Letters. 2018. Vol. 120, num. 22, p. 225901. DOI : 10.1103/PhysRevLett.120.225901.

Hydrogen dynamics in solid formic acid: insights from simulations with quantum colored-noise thermostats

K. Druzbicki; M. Krzystyniak; D. Hollas; V. Kapil; P. Slavicek et al. 

2018-01-01. 7th International Workshop on Electron-Volt Neutron Spectroscopy, Rome, ITALY, Nov 07-08, 2017. p. 012003. DOI : 10.1088/1742-6596/1055/1/012003.

Revealing the Transient Concentration of CO2 in a Mixed-Matrix Membrane by IR Microimaging and Molecular Modeling

S. Hwang; R. Semino; B. Seoane; M. Zahan; C. Chmelik et al. 

Angewandte Chemie-International Edition. 2018. Vol. 57, num. 18, p. 5156-5160. DOI : 10.1002/anie.201713160.

Enhanced gas separation performance of 6FDA-DAM based mixed matrix membranes by incorporating MOF UiO-66 and its derivatives

M. Ahmad; M. Navarro; M. Lhotka; B. Zornoza; C. Tellez et al. 

JOURNAL OF MEMBRANE SCIENCE. 2018. Vol. 558, p. 64-77. DOI : 10.1016/j.memsci.2018.04.040.

Applications of machine learning for studying local solvation environments

M. Groenonboom; S. De; M. Ceriotti; J. Keith 

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.

Early Stages of Precipitation In Aluminum Alloys by First-Principles and Machine-Learning Atomistic Simulations

D. Giofré / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2018. 

Communication: Computing the Tolman length for solid-liquid interfaces

B. Cheng; M. Ceriotti 

The Journal of Chemical Physics. 2018. Vol. 148, num. 23, p. 231102. DOI : 10.1063/1.5038396.

Mapping uncharted territory in ice from zeolite networks to ice structures

E. A. Engel; A. Anelli; M. Ceriotti; C. J. Pickard; R. J. Needs 

Nature Communications. 2018. Vol. 9, num. 1, p. 2173. DOI : 10.1038/s41467-018-04618-6.

Anisotropy of the Proton Momentum Distribution in Water

V. Kapil; A. Cuzzocrea; M. Ceriotti 

The Journal of Physical Chemistry. 2018. Vol. B122, num. 22, p. 6048-6054. DOI : 10.1021/acs.jpcb.8b03896.

Fast-forward Langevin dynamics with momentum flips

M. Hijazi; D. M. Wilkins; M. Ceriotti 

The Journal of Chemical Physics. 2018. Vol. 148, num. 18, p. 184109. DOI : 10.1063/1.5029833.

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

G. Imbalzano; A. Anelli; D. Giofré; S. Klees; J. Behler et al. 

The Journal of Chemical Physics. 2018. Vol. 148, num. 24, p. 241730. DOI : 10.1063/1.5024611.

An Automatic, Data-Driven Definition of Atomic-Scale Structural Motifs

P. Gasparotto / M. Ceriotti (Dir.)  

Lausanne, EPFL, 2018. 

Computing the absolute Gibbs free energy in atomistic simulations: Applications to defects in solids

B. Cheng; M. Ceriotti 

Physical Review B. 2018. Vol. 97, num. 5, p. 054102. DOI : 10.1103/PhysRevB.97.054102.

Recognizing Local and Global Structural Motifs at the Atomic Scale

P. Gasparotto; R. H. Meißner; M. Ceriotti 

Journal of Chemical Theory and Computation. 2018. Vol. 14, num. 2, p. 486-498. DOI : 10.1021/acs.jctc.7b00993.

Machine learning for the structure–energy–property landscapes of molecular crystals

F. Musil; S. De; J. Yang; J. E. Campbell; G. M. Day et al. 

Chemical Science. 2018. Vol. 9, num. 5, p. 1289-1300. DOI : 10.1039/C7SC04665K.

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

A. Grisafi; D. M. Wilkins; G. Csányi; M. Ceriotti 

Physical Review Letters. 2018. Vol. 120, num. 3, p. 036002. DOI : 10.1103/PhysRevLett.120.036002.

Nuclear quantum effects enter the mainstream

T. E. Markland; M. Ceriotti 

Nature Reviews Chemistry. 2018. Vol. 2, num. 3, p. 0109. DOI : 10.1038/s41570-017-0109.

Chemical machine learning with kernels: The key impact of loss functions

V. Q. Nguyen; S. De; J. Lin; V. Cevher 

2018

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

M. Rossi; V. Kapil; M. Ceriotti 

The Journal of Chemical Physics. 2018. Vol. 148, num. 10, p. 102301. DOI : 10.1063/1.4990536.

Machine learning unifies the modeling of materials and molecules

A. P. Bartok; S. De; C. Poelking; N. Bernstein; J. R. Kermode et al. 

Science Advances. 2017. Vol. 3, num. 12, p. e1701816. DOI : 10.1126/sciadv.1701816.

Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires

S. Gabardi; E. Baldi; E. Bosoni; D. Campi; S. Caravati et al. 

Journal Of Physical Chemistry C. 2017. Vol. 121, num. 42, p. 23827-23838. DOI : 10.1021/acs.jpcc.7b09862.

Extracting the interfacial free energy and anisotropy from a smooth fluctuating dividing surface

E. Baldi; M. Ceriotti; G. A. Tribello 

Journal Of Physics-Condensed Matter. 2017. Vol. 29, num. 44, p. 445001. DOI : 10.1088/1361-648X/aa893d.

The Gibbs free energy of homogeneous nucleation: From atomistic nuclei to the planar limit

B. Cheng; G. A. Tribello; M. Ceriotti 

The Journal of Chemical Physics. 2017. Vol. 147, num. 10, p. 104707. DOI : 10.1063/1.4997180.

Solvent fluctuations and nuclear quantum effects modulate the molecular hyperpolarizability of water

C. Liang; G. Tocci; D. M. Wilkins; A. Grisafi; S. Roke et al. 

Physical Review B. 2017. Vol. 96, num. 4, p. 041407(R). DOI : 10.1103/PhysRevB.96.041407.

Nuclear Quantum Effects in Water Reorientation and Hydrogen-Bond Dynamics

D. M. Wilkins; D. E. Manolopoulos; S. Pipolo; D. Laage; J. T. Hynes 

Journal Of Physical Chemistry Letters. 2017. Vol. 8, num. 12, p. 2602-2607. DOI : 10.1021/acs.jpclett.7b00979.

Mapping the conformational free energy of aspartic acid in the gas phase and in aqueous solution

F. Comitani; K. Rossi; M. Ceriotti; M. E. Sanz; C. Molteni 

Journal Of Chemical Physics. 2017. Vol. 146, num. 14, p. 145102. DOI : 10.1063/1.4979519.

Communication: Mean-field theory of water-water correlations in electrolyte solutions

D. M. Wilkins; D. E. Manolopoulos; S. Roke; M. Ceriotti 

The Journal of Chemical Physics. 2017. Vol. 146, num. 18, p. 181103. DOI : 10.1063/1.4983221.

Simulating Energy Relaxation in Pump-Probe Vibrational Spectroscopy of Hydrogen-Bonded Liquids

R. Dettori; M. Ceriotti; J. Hunger; C. Melis; L. Colombo et al. 

Journal Of Chemical Theory And Computation. 2017. Vol. 13, num. 3, p. 1284-1292. DOI : 10.1021/acs.jctc.6b01108.

Structure of a model TiO2 photocatalytic interface

H. Hussain; G. Tocci; T. Woolcot; X. Torrelles; C. L. Pang et al. 

Nature Materials. 2017. Vol. 16, num. 4, p. 461-466. DOI : 10.1038/Nmat4793.

Mapping and classifying molecules from a high-throughput structural database

S. De; F. Musil; T. Ingram; C. Baldauf; M. Ceriotti 

Journal of Cheminformatics. 2017. Vol. 9, num. 1, p. 6. DOI : 10.1186/s13321-017-0192-4.

Bridging the gap between atomistic and macroscopic models of homogeneous nucleation

B. Cheng; M. Ceriotti 

The Journal of Chemical Physics. 2017. Vol. 146, num. 3, p. 034106. DOI : 10.1063/1.4973883.

Electrolytes induce long-range orientational order and free energy changes in the H-bond network of bulk water

Y. Chen; H. I. Okur; N. Gomopoulos; C. Macias-Romero; P. S. Cremer et al. 

Science Advances. 2016-04-01. Vol. 2, num. 4, p. e1501891-e1501891. DOI : 10.1126/sciadv.1501891.

High order path integrals made easy

V. Kapil; J. Behler; M. Ceriotti 

The Journal of Chemical Physics. 2016. Vol. 145, num. 23, p. 234103. DOI : 10.1063/1.4971438.

Second-Harmonic Scattering as a Probe of Structural Correlations in Liquids

G. Tocci; C. Liang; D. M. Wilkins; S. Roke; M. Ceriotti 

The Journal of Physical Chemistry Letters. 2016. Vol. 7, num. 21, p. 4311-4316. DOI : 10.1021/acs.jpclett.6b01851.

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

M. Rossi; M. Ceriotti; D. E. Manolopoulos 

Journal Of Physical Chemistry Letters. 2016. Vol. 7, num. 15, p. 3001-3007. DOI : 10.1021/acs.jpclett.6b01093.

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

F. Uhl; D. Marx; M. Ceriotti 

Journal Of Chemical Physics. 2016. Vol. 145, num. 5, p. 054101. DOI : 10.1063/1.4959602.

Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges

M. Ceriotti; W. Fang; P. G. Kusalik; R. H. Mckenzie; A. Michaelides et al. 

Chemical Reviews. 2016. Vol. 116, num. 13, p. 7529-7550. DOI : 10.1021/acs.chemrev.5b00674.

Thermally-nucleated self-assembly of water and alcohol into stable structures at hydrophobic interfaces

K. Voïtchovsky; D. Giofrè; J. José Segura; F. Stellacci; M. Ceriotti 

Nature Communications. 2016. Vol. 7, p. 13064. DOI : 10.1038/ncomms13064.

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

M. Rossi; P. Gasparotto; M. Ceriotti 

Physical Review Letters. 2016. Vol. 117, num. 11, p. 115702. DOI : 10.1103/PhysRevLett.117.115702.

Vitrification: Machines learn to recognize glasses

M. Ceriotti; V. Vitelli 

Nature Physics. 2016. Vol. 12, num. 5, p. 377-378. DOI : 10.1038/nphys3757.

Electronic transport in B-N substituted bilayer graphene nanojunctions

D. Giofre; D. Ceresoli; G. Fratesi; M. I. Trioni 

Physical Review B. 2016. Vol. 93, num. 20, p. 205420. DOI : 10.1103/PhysRevB.93.205420.

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

B. Cheng; J. Behler; M. Ceriotti 

Journal Of Physical Chemistry Letters. 2016. Vol. 7, num. 12, p. 2210-2215. DOI : 10.1021/acs.jpclett.6b00729.

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

P. Gasparotto; A. A. Hassanali; M. Ceriotti 

Journal of Chemical Theory and Computation. 2016. Vol. 12, num. 4, p. 1953-1964. DOI : 10.1021/acs.jctc.5b01138.

Comparing molecules and solids across structural and alchemical space

S. De; A. P. Bartók; G. Csányi; M. Ceriotti 

Phys. Chem. Chem. Phys.. 2016. Vol. 18, num. 20, p. 13754-13769. DOI : 10.1039/C6CP00415F.

Fast diffusion of water nanodroplets on graphene

M. Ma; G. Tocci; A. Michaelides; G. Aeppli 

Nature Materials. 2016. Vol. 15, num. 1, p. 66-71. DOI : 10.1038/Nmat4449.

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

V. Kapil; J. Vandevondele; M. Ceriotti 

The Journal of Chemical Physics. 2016. Vol. 144, num. 5, p. 054111. DOI : 10.1063/1.4941091.

Beyond Static Structures: Putting Forth REMD as a Tool to Solve Problems in Computational Organic Chemistry

R. Petraglia; A. G. Nicolai; M. Wodrich; M. Ceriotti; C. Corminboeuf 

Journal of Computational Chemistry. 2016. Vol. 37, num. 1, p. 83-92. DOI : 10.1002/jcc.24025.

Solid-liquid interfacial free energy out of equilibrium

B. Cheng; G. A. Tribello; M. Ceriotti 

Physical Review B. 2015. Vol. 92, num. 18, p. 180102. DOI : 10.1103/PhysRevB.92.180102.

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

A. Ardevol; G. A. Tribello; M. Ceriotti; M. Parrinello 

Journal of Chemical Theory and Computation. 2015. Vol. 11, num. 3, p. 1086-1093. DOI : 10.1021/ct500950z.

Modeling the Quantum Nature of Atomic Nuclei by Imaginary Time Path Integrals and Colored Noise

M. Ceriotti 

Computational Trends in Solvation and Transport in Liquids, Lecture Notes; Schriften des Forschungszentrums Jülich, 2015. p. 1-24.

Direct path integral estimators for isotope fractionation ratios

B. Cheng; M. Ceriotti 

Journal Of Chemical Physics. 2014. Vol. 141, num. 24, p. 244112. DOI : 10.1063/1.4904293.

Discussion: Theoretical Horizons and Calculation

M. Ceriotti; C. Drechsel-Grau; F. Fernandez-Alonso; N. Greaves; M. Krzystyniak et al. 

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.

The Role of Quantum Effects on Structural and Electronic Fluctuations in Neat and Charged Water

F. Giberti; A. A. Hassanali; M. Ceriotti; M. Parrinello 

The Journal of Physical Chemistry B. 2014. Vol. 118, num. 46, p. 13226-13235. DOI : 10.1021/jp507752e.

Ab initio simulation of particle momentum distributions in high-pressure water

M. Ceriotti 

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.

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

M. Rossi; H. Liu; F. Paesani; J. Bowman; M. Ceriotti 

The Journal of Chemical Physics. 2014. Vol. 141, num. 18, p. 181101. DOI : 10.1063/1.4901214.

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

P. Gasparotto; M. Ceriotti 

The Journal of Chemical Physics. 2014. Vol. 141, num. 17, p. 174110. DOI : 10.1063/1.4900655.

Quantum fluctuations and isotope effects in ab initio descriptions of water

L. Wang; M. Ceriotti; T. E. Markland 

The Journal of Chemical Physics. 2014. Vol. 141, num. 10, p. 104502. DOI : 10.1063/1.4894287.

How to remove the spurious resonances from ring polymer molecular dynamics

M. Rossi; M. Ceriotti; D. E. Manolopoulos 

The Journal of Chemical Physics. 2014. Vol. 140, num. 23, p. 234116. DOI : 10.1063/1.4883861.

Evaluating functions of positive-definite matrices using colored-noise thermostats

M. Nava; M. Ceriotti; C. Dryzun; M. Parrinello 

Physical Review E. 2014. Vol. 89, num. 2, p. 023302. DOI : 10.1103/PhysRevE.89.023302.

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

M. Ceriotti; J. More; D. E. Manolopoulos 

Computer Physics Communications. 2014. Vol. 185, p. 1019-1026. DOI : 10.1016/j.cpc.2013.10.027.

Effects of High Angular Momentum on the Unimolecular Dissociation of CD2CD2OH: Theory and Comparisons with Experiment

B. G. Mckown; M. Ceriotti; C. C. Womack; E. Kamarchik; L. J. Butler et al. 

Journal Of Physical Chemistry A. 2013. Vol. 117, num. 42, p. 10951-10963. DOI : 10.1021/jp407913t.

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

G. Romanelli; M. Ceriotti; D. E. Manolopoulos; C. Pantalei; R. Senesi et al. 

The Journal of Physical Chemistry Letters. 2013. Vol. 4, num. 19, p. 3251-3256. DOI : 10.1021/jz401538r.

Nuclear quantum effects and hydrogen bond fluctuations in water

M. Ceriotti; J. Cuny; M. Parrinello; D. E. Manolopoulos 

Proceedings of the National Academy of Sciences. 2013. Vol. 110, num. 39, p. 15591-15596. DOI : 10.1073/pnas.1308560110.

A Surface-Specific Isotope Effect in Mixtures of Light and Heavy Water

J. Liu; R. S. Andino; C. M. Miller; X. Chen; D. M. Wilkins et al. 

The Journal of Physical Chemistry C. 2013. Vol. 117, num. 6, p. 2944-2951. DOI : 10.1021/jp311986m.

Efficient methods and practical guidelines for simulating isotope effects

M. Ceriotti; T. E. Markland 

The Journal of Chemical Physics. 2013. Vol. 138, num. 1, p. 014112. DOI : 10.1063/1.4772676.

Demonstrating the Transferability and the Descriptive Power of Sketch-Map

M. Ceriotti; G. A. Tribello; M. Parrinello 

Journal of Chemical Theory and Computation. 2013. Vol. 9, num. 3, p. 1521-1532. DOI : 10.1021/ct3010563.

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

M. Ceriotti; G. A. R. Brain; O. Riordan; D. E. Manolopoulos 

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2012. Vol. 468, num. 2137, p. 2-17. DOI : 10.1098/rspa.2011.0413.

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

G. A. Tribello; M. Ceriotti; M. Parrinello 

Proceedings of the National Academy of Sciences. 2012. Vol. 109, num. 14, p. 5196-5201. DOI : 10.1073/pnas.1201152109.

Simultaneous measurement of lithium and fluorine momentum in

A. G. Seel; M. Ceriotti; P. P. Edwards; J. Mayers 

Journal of Physics: Condensed Matter. 2012. Vol. 24, num. 36, p. 365401. DOI : 10.1088/0953-8984/24/36/365401.

The Fuzzy Quantum Proton in the Hydrogen Chloride Hydrates

A. A. Hassanali; J. Cuny; M. Ceriotti; C. J. Pickard; M. Parrinello 

Journal of the American Chemical Society. 2012. Vol. 134, num. 20, p. 8557-8569. DOI : 10.1021/ja3014727.

Efficient First-Principles Calculation of the Quantum Kinetic Energy and Momentum Distribution of Nuclei

M. Ceriotti; D. E. Manolopoulos 

Physical Review Letters. 2012. Vol. 109, num. 10, p. 100604. DOI : 10.1103/PhysRevLett.109.100604.

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

J. A. Morrone; T. E. Markland; M. Ceriotti; B. J. Berne 

The Journal of Chemical Physics. 2011. Vol. 134, num. 1, p. 014103. DOI : 10.1063/1.3518369.

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

G. Miceli; M. Ceriotti; M. Bernasconi; M. Parrinello 

Physical Review B. 2011. Vol. 83, num. 5, p. 054119. DOI : 10.1103/PhysRevB.83.054119.

First-Principles Study of the High-Temperature Phase of Li

G. Miceli; M. Ceriotti; S. Angioletti-Uberti; M. Bernasconi; M. Parrinello 

The Journal of Physical Chemistry C. 2011. Vol. 115, num. 14, p. 7076-7080. DOI : 10.1021/jp200076p.

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

M. Ceriotti; G. A. Tribello; M. Parrinello 

Proceedings of the National Academy of Sciences. 2011. Vol. 108, num. 32, p. 13023-13028. DOI : 10.1073/pnas.1108486108.

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

M. Ceriotti; D. E. Manolopoulos; M. Parrinello 

The Journal of Chemical Physics. 2011. Vol. 134, num. 8, p. 084104. DOI : 10.1063/1.3556661.

Solid-liquid interface free energy through metadynamics simulations

S. Angioletti-Uberti; M. Ceriotti; P. D. Lee; M. W. Finnis 

Physical Review B. 2010. Vol. 81, num. 12, p. 125416. DOI : 10.1103/PhysRevB.81.125416.

Colored-Noise Thermostats à la Carte

M. Ceriotti; G. Bussi; M. Parrinello 

Journal of Chemical Theory and Computation. 2010. Vol. 6, num. 4, p. 1170-1180. DOI : 10.1021/ct900563s.

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

M. Ceriotti; G. Miceli; A. Pietropaolo; D. Colognesi; A. Nale et al. 

Physical Review B. 2010. Vol. 82, num. 17, p. 174306. DOI : 10.1103/PhysRevB.82.174306.

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

M. Ceriotti; M. Parrinello 

Procedia Computer Science. 2010. Vol. 1, num. 1, p. 1607-1614. DOI : 10.1016/j.procs.2010.04.180.

Efficient stochastic thermostatting of path integral molecular dynamics

M. Ceriotti; M. Parrinello; T. E. Markland; D. E. Manolopoulos 

The Journal of Chemical Physics. 2010. Vol. 133, num. 12, p. 124104. DOI : 10.1063/1.3489925.

A self-learning algorithm for biased molecular dynamics

G. A. Tribello; M. Ceriotti; M. Parrinello 

Proceedings of the National Academy of Sciences. 2010. Vol. 107, num. 41, p. 17509-17514. DOI : 10.1073/pnas.1011511107.

Nuclear Quantum Effects in Solids Using a Colored-Noise Thermostat

M. Ceriotti; G. Bussi; M. Parrinello 

Physical Review Letters. 2009. Vol. 103, num. 3, p. 030603. DOI : 10.1103/PhysRevLett.103.030603.

Langevin Equation with Colored Noise for Constant-Temperature Molecular Dynamics Simulations

M. Ceriotti; G. Bussi; M. Parrinello 

Physical Review Letters. 2009. Vol. 102, num. 2, p. 020601. DOI : 10.1103/PhysRevLett.102.020601.

Ab initio study of the diffusion and decomposition pathways of SiHx species on Si(100)

M. Ceriotti; S. Cereda; F. Montalenti; L. Miglio; M. Bernasconi 

Physical Review B. 2009. Vol. 79, num. 16, p. 165437. DOI : 10.1103/PhysRevB.79.165437.

An efficient and accurate decomposition of the Fermi operator

M. Ceriotti; T. D. KüHne; M. Parrinello 

The Journal of Chemical Physics. 2008. Vol. 129, num. 2, p. 024707. DOI : 10.1063/1.2949515.

First principles study of Ge∕Si exchange mechanisms at the Si(001) surface

F. Zipoli; S. Cereda; M. Ceriotti; M. Bernasconi; L. Miglio et al. 

Applied Physics Letters. 2008. Vol. 92, num. 19, p. 191908. DOI : 10.1063/1.2926683.

Quantitative estimate of H abstraction by thermal SiH3 on hydrogenated Si(001)(2×1)

S. Cereda; M. Ceriotti; F. Montalenti; M. Bernasconi; L. Miglio 

Physical Review B. 2007. Vol. 75, num. 23, p. 235311. DOI : 10.1103/PhysRevB.75.235311.

Diffusion and desorption of SiH3 on hydrogenated H:Si(100)-(2×1) from first principles

M. Ceriotti; M. Bernasconi 

Physical Review B. 2007. Vol. 76, num. 24, p. 245309. DOI : 10.1103/PhysRevB.76.245309.

Conjugate gradient heat bath for ill-conditioned actions

M. Ceriotti; G. Bussi; M. Parrinello 

Physical Review E. 2007. Vol. 76, num. 2, p. 026707. DOI : 10.1103/PhysRevE.76.026707.

Impact-driven effects in thin-film growth: steering and transient mobility at the Ag(110) surface

M. Ceriotti; R. Ferrando; F. Montalenti 

Nanotechnology. 2006. Vol. 17, num. 14, p. 3556-3562. DOI : 10.1088/0957-4484/17/14/033.

Ab initio study of the vibrational properties of crystalline TeO2: The α, β, and γ phases

M. Ceriotti; F. Pietrucci; M. Bernasconi 

Physical Review B. 2006. Vol. 73, num. 10, p. 104304. DOI : 10.1103/PhysRevB.73.104304.