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

Publikacje

Structure-property maps with Kernel principal covariates regression

Autorzy: Benjamin A Helfrecht, Rose K Cersonsky, Guillaume Fraux, Michele Ceriotti
Opublikowane w: Machine Learning: Science and Technology, Numer 1/4, 2020, Strona(/y) 045021, ISSN 2632-2153
Wydawca: IOP
DOI: 10.1088/2632-2153/aba9ef

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

Autorzy: Piero Gasparotto, Davide Bochicchio, Michele Ceriotti, Giovanni M. Pavan
Opublikowane w: The Journal of Physical Chemistry B, Numer 124/3, 2019, Strona(/y) 589-599, ISSN 1520-6106
Wydawca: American Chemical Society
DOI: 10.1021/acs.jpcb.9b11015

Learning the electronic density of states in condensed matter

Autorzy: Chiheb Ben Mahmoud, Andrea Anelli, Gábor Csányi, Michele Ceriotti
Opublikowane w: Physical Review B, Numer 102/23, 2020, ISSN 2469-9950
Wydawca: Physic Rev
DOI: 10.1103/physrevb.102.235130

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

Autorzy: Jack Yang, Sandip De, Josh E. Campbell, Sean Li, Michele Ceriotti, Graeme M. Day
Opublikowane w: Chemistry of Materials, Numer 30/13, 2018, Strona(/y) 4361-4371, ISSN 0897-4756
Wydawca: American Chemical Society
DOI: 10.1021/acs.chemmater.8b01621

Multi-scale approach for the prediction of atomic scale properties

Autorzy: Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Opublikowane w: Chemical Science, Numer 12/6, 2021, Strona(/y) 2078-2090, ISSN 2041-6520
Wydawca: Royal Society of Chemistry
DOI: 10.1039/d0sc04934d

Iterative Unbiasing of Quasi-Equilibrium Sampling

Autorzy: F. Giberti, B. Cheng, G. A. Tribello, M. Ceriotti
Opublikowane w: Journal of Chemical Theory and Computation, Numer 16/1, 2019, Strona(/y) 100-107, ISSN 1549-9618
Wydawca: American Chemical Society
DOI: 10.1021/acs.jctc.9b00907

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

Autorzy: Max Veit, David M. Wilkins, Yang Yang, Robert A. DiStasio, Michele Ceriotti
Opublikowane w: The Journal of Chemical Physics, Numer 153/2, 2020, Strona(/y) 024113, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/5.0009106

Machine learning unifies the modeling of materials and molecules

Autorzy: Albert P. Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R. Kermode, Gábor Csányi, Michele Ceriotti
Opublikowane w: Science Advances, Numer 3/12, 2017, Strona(/y) e1701816, ISSN 2375-2548
Wydawca: AAAS
DOI: 10.1126/sciadv.1701816

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

Autorzy: Felix Musil, Sandip De, Jack Yang, Josh E. Campbell, Graeme Matthew Day, Michele Ceriotti
Opublikowane w: Chemical Science, Numer 9, 2017, Strona(/y) 1289, ISSN 2041-6520
Wydawca: Royal Society of Chemistry
DOI: 10.1039/C7SC04665K

Recognizing Local and Global Structural Motifs at the Atomic Scale

Autorzy: Piero Gasparotto, Robert Horst Meißner, Michele Ceriotti
Opublikowane w: Journal of Chemical Theory and Computation, 2018, ISSN 1549-9618
Wydawca: 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

Autorzy: Thuong T. Nguyen, Eszter Székely, Giulio Imbalzano, Jörg Behler, Gábor Csányi, Michele Ceriotti, Andreas W. Götz, Francesco Paesani
Opublikowane w: The Journal of Chemical Physics, Numer 148/24, 2018, Strona(/y) 241725, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5024577

Generalized convex hull construction for materials discovery

Autorzy: Andrea Anelli, Edgar A. Engel, Chris J. Pickard, Michele Ceriotti
Opublikowane w: Physical Review Materials, Numer 2/10, 2018, ISSN 2475-9953
Wydawca: American Physical Society
DOI: 10.1103/PhysRevMaterials.2.103804

Mapping uncharted territory in ice from zeolite networks to ice structures

Autorzy: Edgar A. Engel, Andrea Anelli, Michele Ceriotti, Chris J. Pickard, Richard J. Needs
Opublikowane w: Nature Communications, Numer 9/1, 2018, ISSN 2041-1723
Wydawca: Nature Publishing Group
DOI: 10.1038/s41467-018-04618-6

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

Autorzy: Yair Litman, Davide Donadio, Michele Ceriotti, Mariana Rossi
Opublikowane w: The Journal of Chemical Physics, Numer 148/10, 2018, Strona(/y) 102320, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5002537

Fast-forward Langevin dynamics with momentum flips

Autorzy: Mahdi Hijazi, David M. Wilkins, Michele Ceriotti
Opublikowane w: The Journal of Chemical Physics, Numer 148/18, 2018, Strona(/y) 184109, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5029833

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

Autorzy: Andrea Grisafi, David M. Wilkins, Gábor Csányi, Michele Ceriotti
Opublikowane w: Physical Review Letters, Numer 120/3, 2018, ISSN 0031-9007
Wydawca: American Physical Society
DOI: 10.1103/PhysRevLett.120.036002

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

Autorzy: Giulio Imbalzano, Andrea Anelli, Daniele Giofré, Sinja Klees, Jörg Behler, Michele Ceriotti
Opublikowane w: The Journal of Chemical Physics, Numer 148/24, 2018, Strona(/y) 241730, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5024611

Nuclear quantum effects enter the mainstream

Autorzy: Thomas E. Markland, Michele Ceriotti
Opublikowane w: Nature Reviews Chemistry, Numer 2/3, 2018, Strona(/y) 0109, ISSN 2397-3358
Wydawca: Springer NAture
DOI: 10.1038/s41570-017-0109

Chemical shifts in molecular solids by machine learning

Autorzy: Federico M. Paruzzo, Albert Hofstetter, Félix Musil, Sandip De, Michele Ceriotti, Lyndon Emsley
Opublikowane w: Nature Communications, Numer 9/1, 2018, ISSN 2041-1723
Wydawca: Nature Publishing Group
DOI: 10.1038/s41467-018-06972-x

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

Autorzy: Bingqing Cheng, Christoph Dellago, Michele Ceriotti
Opublikowane w: Physical Chemistry Chemical Physics, Numer 20/45, 2018, Strona(/y) 28732-28740, ISSN 1463-9076
Wydawca: 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

Autorzy: Michael J. Willatt, Félix Musil, Michele Ceriotti
Opublikowane w: Physical Chemistry Chemical Physics, Numer 20/47, 2018, Strona(/y) 29661-29668, ISSN 1463-9076
Wydawca: Royal Society of Chemistry
DOI: 10.1039/C8CP05921G

Transferable Machine-Learning Model of the Electron Density

Autorzy: Andrea Grisafi, Alberto Fabrizio, Benjamin Meyer, David M. Wilkins, Clemence Corminboeuf, Michele Ceriotti
Opublikowane w: ACS Central Science, Numer 5/1, 2019, Strona(/y) 57-64, ISSN 2374-7943
Wydawca: ACS
DOI: 10.1021/acscentsci.8b00551

Accurate molecular polarizabilities with coupled cluster theory and machine learning

Autorzy: David M. Wilkins, Andrea Grisafi, Yang Yang, Ka Un Lao, Robert A. DiStasio, Michele Ceriotti
Opublikowane w: Proceedings of the National Academy of Sciences, Numer 116/9, 2019, Strona(/y) 3401-3406, ISSN 0027-8424
Wydawca: National Academy of Sciences
DOI: 10.1073/pnas.1816132116

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Autorzy: Michele Ceriotti
Opublikowane w: The Journal of Chemical Physics, Numer 150/15, 2019, Strona(/y) 150901, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5091842

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

Autorzy: 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
Opublikowane w: Computer Physics Communications, Numer 236, 2019, Strona(/y) 214-223, ISSN 0010-4655
Wydawca: Elsevier BV
DOI: 10.1016/j.cpc.2018.09.020

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Autorzy: Félix Musil, Michael J. Willatt, Mikhail A. Langovoy, Michele Ceriotti
Opublikowane w: Journal of Chemical Theory and Computation, Numer 15/2, 2018, Strona(/y) 906-915, ISSN 1549-9618
Wydawca: American Chemical Society
DOI: 10.1021/acs.jctc.8b00959

Ab initio thermodynamics of liquid and solid water

Autorzy: Bingqing Cheng, Edgar A. Engel, Jörg Behler, Christoph Dellago, Michele Ceriotti
Opublikowane w: Proceedings of the National Academy of Sciences, Numer 116/4, 2019, Strona(/y) 1110-1115, ISSN 0027-8424
Wydawca: National Academy of Sciences
DOI: 10.1073/pnas.1815117116

Atom-density representations for machine learning

Autorzy: Michael J. Willatt, Félix Musil, Michele Ceriotti
Opublikowane w: The Journal of Chemical Physics, Numer 150/15, 2019, Strona(/y) 154110, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5090481

A new kind of atlas of zeolite building blocks

Autorzy: Benjamin A. Helfrecht, Rocio Semino, Giovanni Pireddu, Scott M. Auerbach, Michele Ceriotti
Opublikowane w: The Journal of Chemical Physics, Numer 151/15, 2019, Strona(/y) 154112, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5119751

Assessment of Approximate Methods for Anharmonic Free Energies

Autorzy: Venkat Kapil, Edgar Engel, Mariana Rossi, Michele Ceriotti
Opublikowane w: Journal of Chemical Theory and Computation, Numer 15/11, 2019, Strona(/y) 5845-5857, ISSN 1549-9618
Wydawca: American Chemical Society
DOI: 10.1021/acs.jctc.9b00596

A Bayesian approach to NMR crystal structure determination

Autorzy: Edgar A. Engel, Andrea Anelli, Albert Hofstetter, Federico Paruzzo, Lyndon Emsley, Michele Ceriotti
Opublikowane w: Physical Chemistry Chemical Physics, Numer 21/42, 2019, Strona(/y) 23385-23400, ISSN 1463-9076
Wydawca: Royal Society of Chemistry
DOI: 10.1039/c9cp04489b

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

Autorzy: Yang Yang, Ka Un Lao, David M. Wilkins, Andrea Grisafi, Michele Ceriotti, Robert A. DiStasio
Opublikowane w: Scientific Data, Numer 6/1, 2019, ISSN 2052-4463
Wydawca: Springer
DOI: 10.1038/s41597-019-0157-8

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

Autorzy: Nathaniel Raimbault, Andrea Grisafi, Michele Ceriotti, Mariana Rossi
Opublikowane w: New Journal of Physics, Numer 21/10, 2019, Strona(/y) 105001, ISSN 1367-2630
Wydawca: Institute of Physics Publishing
DOI: 10.1088/1367-2630/ab4509

Barely porous organic cages for hydrogen isotope separation

Autorzy: 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
Opublikowane w: Science, Numer 366/6465, 2019, Strona(/y) 613-620, ISSN 0036-8075
Wydawca: American Association for the Advancement of Science
DOI: 10.1126/science.aax7427

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

Autorzy: Benjamin A. Helfrecht, Piero Gasparotto, Federico Giberti, Michele Ceriotti
Opublikowane w: Frontiers in Molecular Biosciences, Numer 6, 2019, ISSN 2296-889X
Wydawca: University College London, United Kingdom
DOI: 10.3389/fmolb.2019.00024

Incorporating long-range physics in atomic-scale machine learning

Autorzy: Andrea Grisafi, Michele Ceriotti
Opublikowane w: The Journal of Chemical Physics, Numer 151/20, 2019, Strona(/y) 204105, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5128375

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

Autorzy: Kislon Voïtchovsky, Daniele Giofrè, Juan José Segura, Francesco Stellacci, Michele Ceriotti
Opublikowane w: Nature Communications, Numer 7, 2016, Strona(/y) 13064, ISSN 2041-1723
Wydawca: Nature Publishing Group
DOI: 10.1038/ncomms13064

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

Autorzy: Venkat Kapil, David M. Wilkins, Jinggang Lan, Michele Ceriotti
Opublikowane w: The Journal of Chemical Physics, Numer 152/12, 2020, Strona(/y) 124104, ISSN 0021-9606
Wydawca: American Institute of Physics
DOI: 10.1063/1.5141950

Chemiscope: interactive structure-property explorer for materials and molecules

Autorzy: Guillaume Fraux, Rose Cersonsky, Michele Ceriotti
Opublikowane w: Journal of Open Source Software, Numer 5/51, 2020, Strona(/y) 2117, ISSN 2475-9066
Wydawca: Independent
DOI: 10.21105/joss.02117

Improving sample and feature selection with principal covariates regression

Autorzy: Rose K Cersonsky, Benjamin A Helfrecht, Edgar A Engel, Sergei Kliavinek, Michele Ceriotti
Opublikowane w: Machine Learning: Science and Technology, Numer 2/3, 2021, Strona(/y) 035038, ISSN 2632-2153
Wydawca: Machine Learning: Science and Technology
DOI: 10.1088/2632-2153/abfe7c

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

Autorzy: F. Giberti, G. A. Tribello, M. Ceriotti
Opublikowane w: Journal of Chemical Theory and Computation, Numer 17/6, 2021, Strona(/y) 3292-3308, ISSN 1549-9618
Wydawca: American Chemical Society
DOI: 10.1021/acs.jctc.0c01177

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

Autorzy: Andrea Grisafi; David M. Wilkins; Michael J. Willatt; Michele Ceriotti
Opublikowane w: ACS Symposium Series, Numer 4, 2019
Wydawca: Machine Learning in Chemistry
DOI: 10.1021/bk-2019-1326.ch001

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