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

Pubblicazioni

Structure-property maps with Kernel principal covariates regression

Autori: Benjamin A Helfrecht, Rose K Cersonsky, Guillaume Fraux, Michele Ceriotti
Pubblicato in: Machine Learning: Science and Technology, Numero 1/4, 2020, Pagina/e 045021, ISSN 2632-2153
Editore: IOP
DOI: 10.1088/2632-2153/aba9ef

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

Autori: Piero Gasparotto, Davide Bochicchio, Michele Ceriotti, Giovanni M. Pavan
Pubblicato in: The Journal of Physical Chemistry B, Numero 124/3, 2019, Pagina/e 589-599, ISSN 1520-6106
Editore: American Chemical Society
DOI: 10.1021/acs.jpcb.9b11015

Learning the electronic density of states in condensed matter

Autori: Chiheb Ben Mahmoud, Andrea Anelli, Gábor Csányi, Michele Ceriotti
Pubblicato in: Physical Review B, Numero 102/23, 2020, ISSN 2469-9950
Editore: Physic Rev
DOI: 10.1103/physrevb.102.235130

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

Autori: Jack Yang, Sandip De, Josh E. Campbell, Sean Li, Michele Ceriotti, Graeme M. Day
Pubblicato in: Chemistry of Materials, Numero 30/13, 2018, Pagina/e 4361-4371, ISSN 0897-4756
Editore: American Chemical Society
DOI: 10.1021/acs.chemmater.8b01621

Multi-scale approach for the prediction of atomic scale properties

Autori: Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Pubblicato in: Chemical Science, Numero 12/6, 2021, Pagina/e 2078-2090, ISSN 2041-6520
Editore: Royal Society of Chemistry
DOI: 10.1039/d0sc04934d

Iterative Unbiasing of Quasi-Equilibrium Sampling

Autori: F. Giberti, B. Cheng, G. A. Tribello, M. Ceriotti
Pubblicato in: Journal of Chemical Theory and Computation, Numero 16/1, 2019, Pagina/e 100-107, ISSN 1549-9618
Editore: American Chemical Society
DOI: 10.1021/acs.jctc.9b00907

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

Autori: Max Veit, David M. Wilkins, Yang Yang, Robert A. DiStasio, Michele Ceriotti
Pubblicato in: The Journal of Chemical Physics, Numero 153/2, 2020, Pagina/e 024113, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/5.0009106

Machine learning unifies the modeling of materials and molecules

Autori: Albert P. Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R. Kermode, Gábor Csányi, Michele Ceriotti
Pubblicato in: Science Advances, Numero 3/12, 2017, Pagina/e e1701816, ISSN 2375-2548
Editore: AAAS
DOI: 10.1126/sciadv.1701816

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

Autori: Felix Musil, Sandip De, Jack Yang, Josh E. Campbell, Graeme Matthew Day, Michele Ceriotti
Pubblicato in: Chemical Science, Numero 9, 2017, Pagina/e 1289, ISSN 2041-6520
Editore: Royal Society of Chemistry
DOI: 10.1039/C7SC04665K

Recognizing Local and Global Structural Motifs at the Atomic Scale

Autori: Piero Gasparotto, Robert Horst Meißner, Michele Ceriotti
Pubblicato in: Journal of Chemical Theory and Computation, 2018, ISSN 1549-9618
Editore: 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

Autori: Thuong T. Nguyen, Eszter Székely, Giulio Imbalzano, Jörg Behler, Gábor Csányi, Michele Ceriotti, Andreas W. Götz, Francesco Paesani
Pubblicato in: The Journal of Chemical Physics, Numero 148/24, 2018, Pagina/e 241725, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5024577

Generalized convex hull construction for materials discovery

Autori: Andrea Anelli, Edgar A. Engel, Chris J. Pickard, Michele Ceriotti
Pubblicato in: Physical Review Materials, Numero 2/10, 2018, ISSN 2475-9953
Editore: American Physical Society
DOI: 10.1103/PhysRevMaterials.2.103804

Mapping uncharted territory in ice from zeolite networks to ice structures

Autori: Edgar A. Engel, Andrea Anelli, Michele Ceriotti, Chris J. Pickard, Richard J. Needs
Pubblicato in: Nature Communications, Numero 9/1, 2018, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-018-04618-6

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

Autori: Yair Litman, Davide Donadio, Michele Ceriotti, Mariana Rossi
Pubblicato in: The Journal of Chemical Physics, Numero 148/10, 2018, Pagina/e 102320, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5002537

Fast-forward Langevin dynamics with momentum flips

Autori: Mahdi Hijazi, David M. Wilkins, Michele Ceriotti
Pubblicato in: The Journal of Chemical Physics, Numero 148/18, 2018, Pagina/e 184109, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5029833

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

Autori: Andrea Grisafi, David M. Wilkins, Gábor Csányi, Michele Ceriotti
Pubblicato in: Physical Review Letters, Numero 120/3, 2018, ISSN 0031-9007
Editore: American Physical Society
DOI: 10.1103/PhysRevLett.120.036002

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

Autori: Giulio Imbalzano, Andrea Anelli, Daniele Giofré, Sinja Klees, Jörg Behler, Michele Ceriotti
Pubblicato in: The Journal of Chemical Physics, Numero 148/24, 2018, Pagina/e 241730, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5024611

Nuclear quantum effects enter the mainstream

Autori: Thomas E. Markland, Michele Ceriotti
Pubblicato in: Nature Reviews Chemistry, Numero 2/3, 2018, Pagina/e 0109, ISSN 2397-3358
Editore: Springer NAture
DOI: 10.1038/s41570-017-0109

Chemical shifts in molecular solids by machine learning

Autori: Federico M. Paruzzo, Albert Hofstetter, Félix Musil, Sandip De, Michele Ceriotti, Lyndon Emsley
Pubblicato in: Nature Communications, Numero 9/1, 2018, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-018-06972-x

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

Autori: Bingqing Cheng, Christoph Dellago, Michele Ceriotti
Pubblicato in: Physical Chemistry Chemical Physics, Numero 20/45, 2018, Pagina/e 28732-28740, ISSN 1463-9076
Editore: 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

Autori: Michael J. Willatt, Félix Musil, Michele Ceriotti
Pubblicato in: Physical Chemistry Chemical Physics, Numero 20/47, 2018, Pagina/e 29661-29668, ISSN 1463-9076
Editore: Royal Society of Chemistry
DOI: 10.1039/C8CP05921G

Transferable Machine-Learning Model of the Electron Density

Autori: Andrea Grisafi, Alberto Fabrizio, Benjamin Meyer, David M. Wilkins, Clemence Corminboeuf, Michele Ceriotti
Pubblicato in: ACS Central Science, Numero 5/1, 2019, Pagina/e 57-64, ISSN 2374-7943
Editore: ACS
DOI: 10.1021/acscentsci.8b00551

Accurate molecular polarizabilities with coupled cluster theory and machine learning

Autori: David M. Wilkins, Andrea Grisafi, Yang Yang, Ka Un Lao, Robert A. DiStasio, Michele Ceriotti
Pubblicato in: Proceedings of the National Academy of Sciences, Numero 116/9, 2019, Pagina/e 3401-3406, ISSN 0027-8424
Editore: National Academy of Sciences
DOI: 10.1073/pnas.1816132116

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Autori: Michele Ceriotti
Pubblicato in: The Journal of Chemical Physics, Numero 150/15, 2019, Pagina/e 150901, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5091842

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

Autori: 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
Pubblicato in: Computer Physics Communications, Numero 236, 2019, Pagina/e 214-223, ISSN 0010-4655
Editore: Elsevier BV
DOI: 10.1016/j.cpc.2018.09.020

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Autori: Félix Musil, Michael J. Willatt, Mikhail A. Langovoy, Michele Ceriotti
Pubblicato in: Journal of Chemical Theory and Computation, Numero 15/2, 2018, Pagina/e 906-915, ISSN 1549-9618
Editore: American Chemical Society
DOI: 10.1021/acs.jctc.8b00959

Ab initio thermodynamics of liquid and solid water

Autori: Bingqing Cheng, Edgar A. Engel, Jörg Behler, Christoph Dellago, Michele Ceriotti
Pubblicato in: Proceedings of the National Academy of Sciences, Numero 116/4, 2019, Pagina/e 1110-1115, ISSN 0027-8424
Editore: National Academy of Sciences
DOI: 10.1073/pnas.1815117116

Atom-density representations for machine learning

Autori: Michael J. Willatt, Félix Musil, Michele Ceriotti
Pubblicato in: The Journal of Chemical Physics, Numero 150/15, 2019, Pagina/e 154110, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5090481

A new kind of atlas of zeolite building blocks

Autori: Benjamin A. Helfrecht, Rocio Semino, Giovanni Pireddu, Scott M. Auerbach, Michele Ceriotti
Pubblicato in: The Journal of Chemical Physics, Numero 151/15, 2019, Pagina/e 154112, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5119751

Assessment of Approximate Methods for Anharmonic Free Energies

Autori: Venkat Kapil, Edgar Engel, Mariana Rossi, Michele Ceriotti
Pubblicato in: Journal of Chemical Theory and Computation, Numero 15/11, 2019, Pagina/e 5845-5857, ISSN 1549-9618
Editore: American Chemical Society
DOI: 10.1021/acs.jctc.9b00596

A Bayesian approach to NMR crystal structure determination

Autori: Edgar A. Engel, Andrea Anelli, Albert Hofstetter, Federico Paruzzo, Lyndon Emsley, Michele Ceriotti
Pubblicato in: Physical Chemistry Chemical Physics, Numero 21/42, 2019, Pagina/e 23385-23400, ISSN 1463-9076
Editore: Royal Society of Chemistry
DOI: 10.1039/c9cp04489b

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

Autori: Yang Yang, Ka Un Lao, David M. Wilkins, Andrea Grisafi, Michele Ceriotti, Robert A. DiStasio
Pubblicato in: Scientific Data, Numero 6/1, 2019, ISSN 2052-4463
Editore: Springer
DOI: 10.1038/s41597-019-0157-8

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

Autori: Nathaniel Raimbault, Andrea Grisafi, Michele Ceriotti, Mariana Rossi
Pubblicato in: New Journal of Physics, Numero 21/10, 2019, Pagina/e 105001, ISSN 1367-2630
Editore: Institute of Physics Publishing
DOI: 10.1088/1367-2630/ab4509

Barely porous organic cages for hydrogen isotope separation

Autori: 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
Pubblicato in: Science, Numero 366/6465, 2019, Pagina/e 613-620, ISSN 0036-8075
Editore: American Association for the Advancement of Science
DOI: 10.1126/science.aax7427

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

Autori: Benjamin A. Helfrecht, Piero Gasparotto, Federico Giberti, Michele Ceriotti
Pubblicato in: Frontiers in Molecular Biosciences, Numero 6, 2019, ISSN 2296-889X
Editore: University College London, United Kingdom
DOI: 10.3389/fmolb.2019.00024

Incorporating long-range physics in atomic-scale machine learning

Autori: Andrea Grisafi, Michele Ceriotti
Pubblicato in: The Journal of Chemical Physics, Numero 151/20, 2019, Pagina/e 204105, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5128375

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

Autori: Kislon Voïtchovsky, Daniele Giofrè, Juan José Segura, Francesco Stellacci, Michele Ceriotti
Pubblicato in: Nature Communications, Numero 7, 2016, Pagina/e 13064, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/ncomms13064

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

Autori: Venkat Kapil, David M. Wilkins, Jinggang Lan, Michele Ceriotti
Pubblicato in: The Journal of Chemical Physics, Numero 152/12, 2020, Pagina/e 124104, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/1.5141950

Chemiscope: interactive structure-property explorer for materials and molecules

Autori: Guillaume Fraux, Rose Cersonsky, Michele Ceriotti
Pubblicato in: Journal of Open Source Software, Numero 5/51, 2020, Pagina/e 2117, ISSN 2475-9066
Editore: Independent
DOI: 10.21105/joss.02117

Improving sample and feature selection with principal covariates regression

Autori: Rose K Cersonsky, Benjamin A Helfrecht, Edgar A Engel, Sergei Kliavinek, Michele Ceriotti
Pubblicato in: Machine Learning: Science and Technology, Numero 2/3, 2021, Pagina/e 035038, ISSN 2632-2153
Editore: Machine Learning: Science and Technology
DOI: 10.1088/2632-2153/abfe7c

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

Autori: F. Giberti, G. A. Tribello, M. Ceriotti
Pubblicato in: Journal of Chemical Theory and Computation, Numero 17/6, 2021, Pagina/e 3292-3308, ISSN 1549-9618
Editore: American Chemical Society
DOI: 10.1021/acs.jctc.0c01177

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

Autori: Andrea Grisafi; David M. Wilkins; Michael J. Willatt; Michele Ceriotti
Pubblicato in: ACS Symposium Series, Numero 4, 2019
Editore: Machine Learning in Chemistry
DOI: 10.1021/bk-2019-1326.ch001

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