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Showing 1–19 of 19 results
Advanced filters: Author: Michele Ceriotti Clear advanced filters

  • PET-MAD is a fast and lightweight universal machine-learning potential, trained on a small but diverse dataset, that delivers near-quantum accuracy in atomistic simulations for both organic and inorganic bulk materials, surfaces, and molecules.

    • Arslan Mazitov
    • Filippo Bigi
    • Michele Ceriotti
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-14

  • The nature of the bulk hydrated electron has been a challenge for both experiment and theory. Here the authors use a machine-learning model trained on MP2 data to achieve an accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.

    • Jinggang Lan
    • Venkat Kapil
    • Vladimir V. Rybkin
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-6

  • Simulations using machine-learning-based interatomic potentials in dense hydrogen overcome system size and timescale limitations, providing evidence of a supercritical behaviour of high-pressure liquid hydrogen and reconciling theoretical and experimental discrepancies.

    • Bingqing Cheng
    • Guglielmo Mazzola
    • Michele Ceriotti
    Research
    Nature
    Volume: 585, P: 217-220

  • The dynamics of a viscous liquid undergo a dramatic slowdown when it is cooled to form a solid glass. Recognizing the structural changes across such a transition remains a major challenge. Machine-learning methods, similar to those Facebook uses to recognize groups of friends, have now been applied to this problem.

    • Michele Ceriotti
    • Vincenzo Vitelli
    News & Views
    Nature Physics
    Volume: 12, P: 377-378

  • A global network of researchers was formed to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity; this paper reports 13 genome-wide significant loci and potentially actionable mechanisms in response to infection.

    • Mari E. K. Niemi
    • Juha Karjalainen
    • Chloe Donohue
    ResearchOpen Access
    Nature
    Volume: 600, P: 472-477

  • Solid-state nuclear magnetic resonance combined with quantum chemical shift predictions is limited by high computational cost. Here, the authors use machine learning based on local atomic environments to predict experimental chemical shifts in molecular solids with accuracy similar to density functional theory.

    • Federico M. Paruzzo
    • Albert Hofstetter
    • Lyndon Emsley
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-10

  • The approximation underlying most atomistic simulations to treat nuclei classically can lead to large errors and the failure to capture important physical effects. This Review reports on recent developments that enable modelling of quantum nuclei at a computational cost comparable with that of a classical simulation.

    • Thomas E. Markland
    • Michele Ceriotti
    Reviews
    Nature Reviews Chemistry
    Volume: 2, P: 1-14

  • Machine learning models enable atomistic simulations of phase transitions in amorphous silicon, predict electronic fingerprints, and show that the pressure-induced crystallization occurs over three distinct stages.

    • Volker L. Deringer
    • Noam Bernstein
    • Stephen R. Elliott
    Research
    Nature
    Volume: 589, P: 59-64

  • Zeolite membranes can be used for gas molecular sieving, but synthesis requires complex hydrothermal treatment. Here, single layers of zeolite precursor RUB-15 are exfoliated followed by a condensation reaction, forming zeolite membranes with H2/CO2 selectivity of 20 to 100 in a facile process.

    • Mostapha Dakhchoune
    • Luis Francisco Villalobos
    • Kumar Varoon Agrawal
    Research
    Nature Materials
    Volume: 20, P: 362-369

  • Ice is one of the most well-studied condensed matter systems, yet new phases are still being discovered. Here the authors report a large-scale computational study of the configuration space of water ice, creating a navigable “sketch-map” including new predicted phases as well as relationships between different structures.

    • Edgar A. Engel
    • Andrea Anelli
    • Richard J. Needs
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-7

  • Alcohol-water mixtures are characterized by the existence of segregated clusters, whose dynamics are too fast to be investigated in bulk solution. Here, Voïtchovsky et al. show the formation of stable two-dimensional water-alcohol wire-like structures via H-bonds on graphite surface at room temperature.

    • Kislon Voïtchovsky
    • Daniele Giofrè
    • Michele Ceriotti
    ResearchOpen Access
    Nature Communications
    Volume: 7, P: 1-9

  • Graphite remains stable at pressures higher than those of its equilibrium coexistence with diamond. This has proved hard to explain, owing to the difficulty in simulating the transition with accuracy. Ab initio calculations using a trained neural-network potential now show that the stability of graphite and the direct transformation of graphite to diamond can be accounted for by a nucleation mechanism.

    • Rustam Z. Khaliullin
    • Hagai Eshet
    • Michele Parrinello
    Research
    Nature Materials
    Volume: 10, P: 693-697

  • The PLUMED consortium unifies developers and contributors to PLUMED, an open-source library for enhanced-sampling, free-energy calculations and the analysis of molecular dynamics simulations. Here, we outline our efforts to promote transparency and reproducibility by disseminating protocols for enhanced-sampling molecular simulations.

    • Massimiliano Bonomi
    • Giovanni Bussi
    • Andrew White
    Comments & Opinion
    Nature Methods
    Volume: 16, P: 670-673