Decoding, Mapping and Designing the Structural Complexity of Hydrogen-Bond Networks: from Water to Proteins to Polymers

Project Information

HBMAP

Grant agreement ID: 677013

DOI 10.3030/677013

Project closed

EC signature date 21 December 2015

Start date 1 May 2016

End date 30 April 2021

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost € 1 500 000,00

EU contribution € 1 500 000,00

Coordinated by

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Switzerland

Publications

Peer reviewed articles (41)

Structure-property maps with Kernel principal covariates regression

Author(s): Benjamin A Helfrecht, Rose K Cersonsky, Guillaume Fraux, Michele Ceriotti
Published in: Machine Learning: Science and Technology, Issue 1/4, 2020, Page(s) 045021, ISSN 2632-2153
Publisher: IOP
DOI: 10.1088/2632-2153/aba9ef

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

Author(s): Piero Gasparotto, Davide Bochicchio, Michele Ceriotti, Giovanni M. Pavan
Published in: The Journal of Physical Chemistry B, Issue 124/3, 2019, Page(s) 589-599, ISSN 1520-6106
Publisher: American Chemical Society
DOI: 10.1021/acs.jpcb.9b11015

Learning the electronic density of states in condensed matter

Author(s): Chiheb Ben Mahmoud, Andrea Anelli, Gábor Csányi, Michele Ceriotti
Published in: Physical Review B, Issue 102/23, 2020, ISSN 2469-9950
Publisher: Physic Rev
DOI: 10.1103/physrevb.102.235130

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

Author(s): Jack Yang, Sandip De, Josh E. Campbell, Sean Li, Michele Ceriotti, Graeme M. Day
Published in: Chemistry of Materials, Issue 30/13, 2018, Page(s) 4361-4371, ISSN 0897-4756
Publisher: American Chemical Society
DOI: 10.1021/acs.chemmater.8b01621

Multi-scale approach for the prediction of atomic scale properties

Author(s): Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Published in: Chemical Science, Issue 12/6, 2021, Page(s) 2078-2090, ISSN 2041-6520
Publisher: Royal Society of Chemistry
DOI: 10.1039/d0sc04934d

Iterative Unbiasing of Quasi-Equilibrium Sampling

Author(s): F. Giberti, B. Cheng, G. A. Tribello, M. Ceriotti
Published in: Journal of Chemical Theory and Computation, Issue 16/1, 2019, Page(s) 100-107, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.9b00907

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

Author(s): Max Veit, David M. Wilkins, Yang Yang, Robert A. DiStasio, Michele Ceriotti
Published in: The Journal of Chemical Physics, Issue 153/2, 2020, Page(s) 024113, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0009106

Machine learning unifies the modeling of materials and molecules

Author(s): Albert P. Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R. Kermode, Gábor Csányi, Michele Ceriotti
Published in: Science Advances, Issue 3/12, 2017, Page(s) e1701816, ISSN 2375-2548
Publisher: AAAS
DOI: 10.1126/sciadv.1701816

Machine Learning for the Structure-Energy-Property Landscapes of Molecular Crystals

Author(s): Felix Musil, Sandip De, Jack Yang, Josh E. Campbell, Graeme Matthew Day, Michele Ceriotti
Published in: Chemical Science, Issue 9, 2017, Page(s) 1289, ISSN 2041-6520
Publisher: Royal Society of Chemistry
DOI: 10.1039/C7SC04665K

Recognizing Local and Global Structural Motifs at the Atomic Scale

Author(s): Piero Gasparotto, Robert Horst Meißner, Michele Ceriotti
Published in: Journal of Chemical Theory and Computation, 2018, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.7b00993

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

Author(s): Thuong T. Nguyen, Eszter Székely, Giulio Imbalzano, Jörg Behler, Gábor Csányi, Michele Ceriotti, Andreas W. Götz, Francesco Paesani
Published in: The Journal of Chemical Physics, Issue 148/24, 2018, Page(s) 241725, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5024577

Generalized convex hull construction for materials discovery

Author(s): Andrea Anelli, Edgar A. Engel, Chris J. Pickard, Michele Ceriotti
Published in: Physical Review Materials, Issue 2/10, 2018, ISSN 2475-9953
Publisher: American Physical Society
DOI: 10.1103/PhysRevMaterials.2.103804

Mapping uncharted territory in ice from zeolite networks to ice structures

Author(s): Edgar A. Engel, Andrea Anelli, Michele Ceriotti, Chris J. Pickard, Richard J. Needs
Published in: Nature Communications, Issue 9/1, 2018, ISSN 2041-1723
Publisher: Nature Publishing Group
DOI: 10.1038/s41467-018-04618-6

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

Author(s): Yair Litman, Davide Donadio, Michele Ceriotti, Mariana Rossi
Published in: The Journal of Chemical Physics, Issue 148/10, 2018, Page(s) 102320, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5002537

Fast-forward Langevin dynamics with momentum flips

Author(s): Mahdi Hijazi, David M. Wilkins, Michele Ceriotti
Published in: The Journal of Chemical Physics, Issue 148/18, 2018, Page(s) 184109, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5029833

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

Author(s): Andrea Grisafi, David M. Wilkins, Gábor Csányi, Michele Ceriotti
Published in: Physical Review Letters, Issue 120/3, 2018, ISSN 0031-9007
Publisher: American Physical Society
DOI: 10.1103/PhysRevLett.120.036002

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

Author(s): Giulio Imbalzano, Andrea Anelli, Daniele Giofré, Sinja Klees, Jörg Behler, Michele Ceriotti
Published in: The Journal of Chemical Physics, Issue 148/24, 2018, Page(s) 241730, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5024611

Nuclear quantum effects enter the mainstream

Author(s): Thomas E. Markland, Michele Ceriotti
Published in: Nature Reviews Chemistry, Issue 2/3, 2018, Page(s) 0109, ISSN 2397-3358
Publisher: Springer NAture
DOI: 10.1038/s41570-017-0109

Chemical shifts in molecular solids by machine learning

Author(s): Federico M. Paruzzo, Albert Hofstetter, Félix Musil, Sandip De, Michele Ceriotti, Lyndon Emsley
Published in: Nature Communications, Issue 9/1, 2018, ISSN 2041-1723
Publisher: Nature Publishing Group
DOI: 10.1038/s41467-018-06972-x

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

Author(s): Bingqing Cheng, Christoph Dellago, Michele Ceriotti
Published in: Physical Chemistry Chemical Physics, Issue 20/45, 2018, Page(s) 28732-28740, ISSN 1463-9076
Publisher: Royal Society of Chemistry
DOI: 10.1039/C8CP04561E

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

Author(s): Michael J. Willatt, Félix Musil, Michele Ceriotti
Published in: Physical Chemistry Chemical Physics, Issue 20/47, 2018, Page(s) 29661-29668, ISSN 1463-9076
Publisher: Royal Society of Chemistry
DOI: 10.1039/C8CP05921G

Transferable Machine-Learning Model of the Electron Density

Author(s): Andrea Grisafi, Alberto Fabrizio, Benjamin Meyer, David M. Wilkins, Clemence Corminboeuf, Michele Ceriotti
Published in: ACS Central Science, Issue 5/1, 2019, Page(s) 57-64, ISSN 2374-7943
Publisher: ACS
DOI: 10.1021/acscentsci.8b00551

Accurate molecular polarizabilities with coupled cluster theory and machine learning

Author(s): David M. Wilkins, Andrea Grisafi, Yang Yang, Ka Un Lao, Robert A. DiStasio, Michele Ceriotti
Published in: Proceedings of the National Academy of Sciences, Issue 116/9, 2019, Page(s) 3401-3406, ISSN 0027-8424
Publisher: National Academy of Sciences
DOI: 10.1073/pnas.1816132116

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Author(s): Michele Ceriotti
Published in: The Journal of Chemical Physics, Issue 150/15, 2019, Page(s) 150901, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5091842

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

Author(s): Venkat Kapil, Mariana Rossi, Ondrej Marsalek, Riccardo Petraglia, Yair Litman, Thomas Spura, Bingqing Cheng, Alice Cuzzocrea, Robert H. Meißner, David M. Wilkins, Benjamin A. Helfrecht, Przemysław Juda, Sébastien P. Bienvenue, Wei Fang, Jan Kessler, Igor Poltavsky, Steven Vandenbrande, Jelle Wieme, Clemence Corminboeuf, Thomas D. Kühne, David E. Manolopoulos, Thomas E. Markland, Jeremy O. Rich
Published in: Computer Physics Communications, Issue 236, 2019, Page(s) 214-223, ISSN 0010-4655
Publisher: Elsevier BV
DOI: 10.1016/j.cpc.2018.09.020

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Author(s): Félix Musil, Michael J. Willatt, Mikhail A. Langovoy, Michele Ceriotti
Published in: Journal of Chemical Theory and Computation, Issue 15/2, 2018, Page(s) 906-915, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.8b00959

Ab initio thermodynamics of liquid and solid water

Author(s): Bingqing Cheng, Edgar A. Engel, Jörg Behler, Christoph Dellago, Michele Ceriotti
Published in: Proceedings of the National Academy of Sciences, Issue 116/4, 2019, Page(s) 1110-1115, ISSN 0027-8424
Publisher: National Academy of Sciences
DOI: 10.1073/pnas.1815117116

Atom-density representations for machine learning

Author(s): Michael J. Willatt, Félix Musil, Michele Ceriotti
Published in: The Journal of Chemical Physics, Issue 150/15, 2019, Page(s) 154110, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5090481

A new kind of atlas of zeolite building blocks

Author(s): Benjamin A. Helfrecht, Rocio Semino, Giovanni Pireddu, Scott M. Auerbach, Michele Ceriotti
Published in: The Journal of Chemical Physics, Issue 151/15, 2019, Page(s) 154112, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5119751

Assessment of Approximate Methods for Anharmonic Free Energies

Author(s): Venkat Kapil, Edgar Engel, Mariana Rossi, Michele Ceriotti
Published in: Journal of Chemical Theory and Computation, Issue 15/11, 2019, Page(s) 5845-5857, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.9b00596

A Bayesian approach to NMR crystal structure determination

Author(s): Edgar A. Engel, Andrea Anelli, Albert Hofstetter, Federico Paruzzo, Lyndon Emsley, Michele Ceriotti
Published in: Physical Chemistry Chemical Physics, Issue 21/42, 2019, Page(s) 23385-23400, ISSN 1463-9076
Publisher: Royal Society of Chemistry
DOI: 10.1039/c9cp04489b

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

Author(s): Yang Yang, Ka Un Lao, David M. Wilkins, Andrea Grisafi, Michele Ceriotti, Robert A. DiStasio
Published in: Scientific Data, Issue 6/1, 2019, ISSN 2052-4463
Publisher: Springer
DOI: 10.1038/s41597-019-0157-8

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

Author(s): Nathaniel Raimbault, Andrea Grisafi, Michele Ceriotti, Mariana Rossi
Published in: New Journal of Physics, Issue 21/10, 2019, Page(s) 105001, ISSN 1367-2630
Publisher: Institute of Physics Publishing
DOI: 10.1088/1367-2630/ab4509

Barely porous organic cages for hydrogen isotope separation

Author(s): Ming Liu, Linda Zhang, Marc A. Little, Venkat Kapil, Michele Ceriotti, Siyuan Yang, Lifeng Ding, Daniel L. Holden, Rafael Balderas-Xicohténcatl, Donglin He, Rob Clowes, Samantha Y. Chong, Gisela Schütz, Linjiang Chen, Michael Hirscher, Andrew I. Cooper
Published in: Science, Issue 366/6465, 2019, Page(s) 613-620, ISSN 0036-8075
Publisher: American Association for the Advancement of Science
DOI: 10.1126/science.aax7427

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

Author(s): Benjamin A. Helfrecht, Piero Gasparotto, Federico Giberti, Michele Ceriotti
Published in: Frontiers in Molecular Biosciences, Issue 6, 2019, ISSN 2296-889X
Publisher: University College London, United Kingdom
DOI: 10.3389/fmolb.2019.00024

Incorporating long-range physics in atomic-scale machine learning

Author(s): Andrea Grisafi, Michele Ceriotti
Published in: The Journal of Chemical Physics, Issue 151/20, 2019, Page(s) 204105, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5128375

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

Author(s): Kislon Voïtchovsky, Daniele Giofrè, Juan José Segura, Francesco Stellacci, Michele Ceriotti
Published in: Nature Communications, Issue 7, 2016, Page(s) 13064, ISSN 2041-1723
Publisher: Nature Publishing Group
DOI: 10.1038/ncomms13064

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

Author(s): Venkat Kapil, David M. Wilkins, Jinggang Lan, Michele Ceriotti
Published in: The Journal of Chemical Physics, Issue 152/12, 2020, Page(s) 124104, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/1.5141950

Chemiscope: interactive structure-property explorer for materials and molecules

Author(s): Guillaume Fraux, Rose Cersonsky, Michele Ceriotti
Published in: Journal of Open Source Software, Issue 5/51, 2020, Page(s) 2117, ISSN 2475-9066
Publisher: Independent
DOI: 10.21105/joss.02117

Improving sample and feature selection with principal covariates regression

Author(s): Rose K Cersonsky, Benjamin A Helfrecht, Edgar A Engel, Sergei Kliavinek, Michele Ceriotti
Published in: Machine Learning: Science and Technology, Issue 2/3, 2021, Page(s) 035038, ISSN 2632-2153
Publisher: Machine Learning: Science and Technology
DOI: 10.1088/2632-2153/abfe7c

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

Author(s): F. Giberti, G. A. Tribello, M. Ceriotti
Published in: Journal of Chemical Theory and Computation, Issue 17/6, 2021, Page(s) 3292-3308, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.0c01177

Book chapters (1)

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

Author(s): Andrea Grisafi; David M. Wilkins; Michael J. Willatt; Michele Ceriotti
Published in: ACS Symposium Series, Issue 4, 2019
Publisher: Machine Learning in Chemistry
DOI: 10.1021/bk-2019-1326.ch001