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

Información del proyecto

HBMAP

Identificador del acuerdo de subvención: 677013

DOI

10.3030/677013

Proyecto cerrado

Fecha de la firma de la CE 21 Diciembre 2015

Fecha de inicio 1 Mayo 2016

Fecha de finalización 30 Abril 2021

Financiado con arreglo a

• EXCELLENT SCIENCE - European Research Council (ERC)

Coste total

€ 1 500 000,00

Aportación de la UE

€ 1 500 000,00

Coordinado por

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Switzerland

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Publicaciones

Peer reviewed articles (41)

Structure-property maps with Kernel principal covariates regression

Autores: Benjamin A Helfrecht, Rose K Cersonsky, Guillaume Fraux, Michele Ceriotti
Publicado en: Machine Learning: Science and Technology, Edición 1/4, 2020, Página(s) 045021, ISSN 2632-2153
Editor: IOP
DOI: 10.1088/2632-2153/aba9ef

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

Autores: Piero Gasparotto, Davide Bochicchio, Michele Ceriotti, Giovanni M. Pavan
Publicado en: The Journal of Physical Chemistry B, Edición 124/3, 2019, Página(s) 589-599, ISSN 1520-6106
Editor: American Chemical Society
DOI: 10.1021/acs.jpcb.9b11015

Learning the electronic density of states in condensed matter

Autores: Chiheb Ben Mahmoud, Andrea Anelli, Gábor Csányi, Michele Ceriotti
Publicado en: Physical Review B, Edición 102/23, 2020, ISSN 2469-9950
Editor: Physic Rev
DOI: 10.1103/physrevb.102.235130

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

Autores: Jack Yang, Sandip De, Josh E. Campbell, Sean Li, Michele Ceriotti, Graeme M. Day
Publicado en: Chemistry of Materials, Edición 30/13, 2018, Página(s) 4361-4371, ISSN 0897-4756
Editor: American Chemical Society
DOI: 10.1021/acs.chemmater.8b01621

Multi-scale approach for the prediction of atomic scale properties

Autores: Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Publicado en: Chemical Science, Edición 12/6, 2021, Página(s) 2078-2090, ISSN 2041-6520
Editor: Royal Society of Chemistry
DOI: 10.1039/d0sc04934d

Iterative Unbiasing of Quasi-Equilibrium Sampling

Autores: F. Giberti, B. Cheng, G. A. Tribello, M. Ceriotti
Publicado en: Journal of Chemical Theory and Computation, Edición 16/1, 2019, Página(s) 100-107, ISSN 1549-9618
Editor: American Chemical Society
DOI: 10.1021/acs.jctc.9b00907

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

Autores: Max Veit, David M. Wilkins, Yang Yang, Robert A. DiStasio, Michele Ceriotti
Publicado en: The Journal of Chemical Physics, Edición 153/2, 2020, Página(s) 024113, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/5.0009106

Machine learning unifies the modeling of materials and molecules

Autores: Albert P. Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R. Kermode, Gábor Csányi, Michele Ceriotti
Publicado en: Science Advances, Edición 3/12, 2017, Página(s) e1701816, ISSN 2375-2548
Editor: AAAS
DOI: 10.1126/sciadv.1701816

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

Autores: Felix Musil, Sandip De, Jack Yang, Josh E. Campbell, Graeme Matthew Day, Michele Ceriotti
Publicado en: Chemical Science, Edición 9, 2017, Página(s) 1289, ISSN 2041-6520
Editor: Royal Society of Chemistry
DOI: 10.1039/C7SC04665K

Recognizing Local and Global Structural Motifs at the Atomic Scale

Autores: Piero Gasparotto, Robert Horst Meißner, Michele Ceriotti
Publicado en: Journal of Chemical Theory and Computation, 2018, ISSN 1549-9618
Editor: 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

Autores: Thuong T. Nguyen, Eszter Székely, Giulio Imbalzano, Jörg Behler, Gábor Csányi, Michele Ceriotti, Andreas W. Götz, Francesco Paesani
Publicado en: The Journal of Chemical Physics, Edición 148/24, 2018, Página(s) 241725, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5024577

Generalized convex hull construction for materials discovery

Autores: Andrea Anelli, Edgar A. Engel, Chris J. Pickard, Michele Ceriotti
Publicado en: Physical Review Materials, Edición 2/10, 2018, ISSN 2475-9953
Editor: American Physical Society
DOI: 10.1103/PhysRevMaterials.2.103804

Mapping uncharted territory in ice from zeolite networks to ice structures

Autores: Edgar A. Engel, Andrea Anelli, Michele Ceriotti, Chris J. Pickard, Richard J. Needs
Publicado en: Nature Communications, Edición 9/1, 2018, ISSN 2041-1723
Editor: Nature Publishing Group
DOI: 10.1038/s41467-018-04618-6

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

Autores: Yair Litman, Davide Donadio, Michele Ceriotti, Mariana Rossi
Publicado en: The Journal of Chemical Physics, Edición 148/10, 2018, Página(s) 102320, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5002537

Fast-forward Langevin dynamics with momentum flips

Autores: Mahdi Hijazi, David M. Wilkins, Michele Ceriotti
Publicado en: The Journal of Chemical Physics, Edición 148/18, 2018, Página(s) 184109, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5029833

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

Autores: Andrea Grisafi, David M. Wilkins, Gábor Csányi, Michele Ceriotti
Publicado en: Physical Review Letters, Edición 120/3, 2018, ISSN 0031-9007
Editor: American Physical Society
DOI: 10.1103/PhysRevLett.120.036002

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

Autores: Giulio Imbalzano, Andrea Anelli, Daniele Giofré, Sinja Klees, Jörg Behler, Michele Ceriotti
Publicado en: The Journal of Chemical Physics, Edición 148/24, 2018, Página(s) 241730, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5024611

Nuclear quantum effects enter the mainstream

Autores: Thomas E. Markland, Michele Ceriotti
Publicado en: Nature Reviews Chemistry, Edición 2/3, 2018, Página(s) 0109, ISSN 2397-3358
Editor: Springer NAture
DOI: 10.1038/s41570-017-0109

Chemical shifts in molecular solids by machine learning

Autores: Federico M. Paruzzo, Albert Hofstetter, Félix Musil, Sandip De, Michele Ceriotti, Lyndon Emsley
Publicado en: Nature Communications, Edición 9/1, 2018, ISSN 2041-1723
Editor: Nature Publishing Group
DOI: 10.1038/s41467-018-06972-x

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

Autores: Bingqing Cheng, Christoph Dellago, Michele Ceriotti
Publicado en: Physical Chemistry Chemical Physics, Edición 20/45, 2018, Página(s) 28732-28740, ISSN 1463-9076
Editor: 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

Autores: Michael J. Willatt, Félix Musil, Michele Ceriotti
Publicado en: Physical Chemistry Chemical Physics, Edición 20/47, 2018, Página(s) 29661-29668, ISSN 1463-9076
Editor: Royal Society of Chemistry
DOI: 10.1039/C8CP05921G

Transferable Machine-Learning Model of the Electron Density

Autores: Andrea Grisafi, Alberto Fabrizio, Benjamin Meyer, David M. Wilkins, Clemence Corminboeuf, Michele Ceriotti
Publicado en: ACS Central Science, Edición 5/1, 2019, Página(s) 57-64, ISSN 2374-7943
Editor: ACS
DOI: 10.1021/acscentsci.8b00551

Accurate molecular polarizabilities with coupled cluster theory and machine learning

Autores: David M. Wilkins, Andrea Grisafi, Yang Yang, Ka Un Lao, Robert A. DiStasio, Michele Ceriotti
Publicado en: Proceedings of the National Academy of Sciences, Edición 116/9, 2019, Página(s) 3401-3406, ISSN 0027-8424
Editor: National Academy of Sciences
DOI: 10.1073/pnas.1816132116

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Autores: Michele Ceriotti
Publicado en: The Journal of Chemical Physics, Edición 150/15, 2019, Página(s) 150901, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5091842

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

Autores: 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
Publicado en: Computer Physics Communications, Edición 236, 2019, Página(s) 214-223, ISSN 0010-4655
Editor: Elsevier BV
DOI: 10.1016/j.cpc.2018.09.020

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Autores: Félix Musil, Michael J. Willatt, Mikhail A. Langovoy, Michele Ceriotti
Publicado en: Journal of Chemical Theory and Computation, Edición 15/2, 2018, Página(s) 906-915, ISSN 1549-9618
Editor: American Chemical Society
DOI: 10.1021/acs.jctc.8b00959

Ab initio thermodynamics of liquid and solid water

Autores: Bingqing Cheng, Edgar A. Engel, Jörg Behler, Christoph Dellago, Michele Ceriotti
Publicado en: Proceedings of the National Academy of Sciences, Edición 116/4, 2019, Página(s) 1110-1115, ISSN 0027-8424
Editor: National Academy of Sciences
DOI: 10.1073/pnas.1815117116

Atom-density representations for machine learning

Autores: Michael J. Willatt, Félix Musil, Michele Ceriotti
Publicado en: The Journal of Chemical Physics, Edición 150/15, 2019, Página(s) 154110, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5090481

A new kind of atlas of zeolite building blocks

Autores: Benjamin A. Helfrecht, Rocio Semino, Giovanni Pireddu, Scott M. Auerbach, Michele Ceriotti
Publicado en: The Journal of Chemical Physics, Edición 151/15, 2019, Página(s) 154112, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5119751

Assessment of Approximate Methods for Anharmonic Free Energies

Autores: Venkat Kapil, Edgar Engel, Mariana Rossi, Michele Ceriotti
Publicado en: Journal of Chemical Theory and Computation, Edición 15/11, 2019, Página(s) 5845-5857, ISSN 1549-9618
Editor: American Chemical Society
DOI: 10.1021/acs.jctc.9b00596

A Bayesian approach to NMR crystal structure determination

Autores: Edgar A. Engel, Andrea Anelli, Albert Hofstetter, Federico Paruzzo, Lyndon Emsley, Michele Ceriotti
Publicado en: Physical Chemistry Chemical Physics, Edición 21/42, 2019, Página(s) 23385-23400, ISSN 1463-9076
Editor: Royal Society of Chemistry
DOI: 10.1039/c9cp04489b

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

Autores: Yang Yang, Ka Un Lao, David M. Wilkins, Andrea Grisafi, Michele Ceriotti, Robert A. DiStasio
Publicado en: Scientific Data, Edición 6/1, 2019, ISSN 2052-4463
Editor: Springer
DOI: 10.1038/s41597-019-0157-8

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

Autores: Nathaniel Raimbault, Andrea Grisafi, Michele Ceriotti, Mariana Rossi
Publicado en: New Journal of Physics, Edición 21/10, 2019, Página(s) 105001, ISSN 1367-2630
Editor: Institute of Physics Publishing
DOI: 10.1088/1367-2630/ab4509

Barely porous organic cages for hydrogen isotope separation

Autores: 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
Publicado en: Science, Edición 366/6465, 2019, Página(s) 613-620, ISSN 0036-8075
Editor: American Association for the Advancement of Science
DOI: 10.1126/science.aax7427

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

Autores: Benjamin A. Helfrecht, Piero Gasparotto, Federico Giberti, Michele Ceriotti
Publicado en: Frontiers in Molecular Biosciences, Edición 6, 2019, ISSN 2296-889X
Editor: University College London, United Kingdom
DOI: 10.3389/fmolb.2019.00024

Incorporating long-range physics in atomic-scale machine learning

Autores: Andrea Grisafi, Michele Ceriotti
Publicado en: The Journal of Chemical Physics, Edición 151/20, 2019, Página(s) 204105, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5128375

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

Autores: Kislon Voïtchovsky, Daniele Giofrè, Juan José Segura, Francesco Stellacci, Michele Ceriotti
Publicado en: Nature Communications, Edición 7, 2016, Página(s) 13064, ISSN 2041-1723
Editor: Nature Publishing Group
DOI: 10.1038/ncomms13064

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

Autores: Venkat Kapil, David M. Wilkins, Jinggang Lan, Michele Ceriotti
Publicado en: The Journal of Chemical Physics, Edición 152/12, 2020, Página(s) 124104, ISSN 0021-9606
Editor: American Institute of Physics
DOI: 10.1063/1.5141950

Chemiscope: interactive structure-property explorer for materials and molecules

Autores: Guillaume Fraux, Rose Cersonsky, Michele Ceriotti
Publicado en: Journal of Open Source Software, Edición 5/51, 2020, Página(s) 2117, ISSN 2475-9066
Editor: Independent
DOI: 10.21105/joss.02117

Improving sample and feature selection with principal covariates regression

Autores: Rose K Cersonsky, Benjamin A Helfrecht, Edgar A Engel, Sergei Kliavinek, Michele Ceriotti
Publicado en: Machine Learning: Science and Technology, Edición 2/3, 2021, Página(s) 035038, ISSN 2632-2153
Editor: Machine Learning: Science and Technology
DOI: 10.1088/2632-2153/abfe7c

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

Autores: F. Giberti, G. A. Tribello, M. Ceriotti
Publicado en: Journal of Chemical Theory and Computation, Edición 17/6, 2021, Página(s) 3292-3308, ISSN 1549-9618
Editor: American Chemical Society
DOI: 10.1021/acs.jctc.0c01177

Book chapters (1)

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

Autores: Andrea Grisafi; David M. Wilkins; Michael J. Willatt; Michele Ceriotti
Publicado en: ACS Symposium Series, Edición 4, 2019
Editor: Machine Learning in Chemistry
DOI: 10.1021/bk-2019-1326.ch001