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Showing 1–16 of 16 results
Advanced filters: Author: Volker L. Deringer Clear advanced filters

  • Exascale computers — supercomputers that can perform 1018 floating point operations per second — started coming online in 2022: in the United States, Frontier launched as the first public exascale supercomputer and Aurora is due to open soon; OceanLight and Tianhe-3 are operational in China; and JUPITER is due to launch in 2023 in Europe. Supercomputers offer unprecedented opportunities for modelling complex materials. In this Viewpoint, five researchers working on different types of materials discuss the most promising directions in computational materials science.

    • Choongseok Chang
    • Volker L. Deringer
    • Christopher M. Wolverton
    Reviews
    Nature Reviews Materials
    Volume: 8, P: 309-313

  • Machine learning is revolutionising materials modelling but requires high-quality training data. Here, the authors introduce autoplex, an open framework automating exploration and fitting of potential-energy surfaces across diverse materials.

    • Yuanbin Liu
    • Joe D. Morrow
    • Volker L. Deringer
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-12

  • Atomistic simulations are important for phase-change materials and devices. Here, the authors present fast and accurate machine-learned potentials, enabling full-cycle device-scale simulations and showcasing applications in studying memory and neuromorphic computing devices.

    • Yuxing Zhou
    • Daniel F. Thomas du Toit
    • Volker L. Deringer
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-12

  • A low-temperature growth technique yields wafer-scale, nanometre-thin samples of amorphous noble metal selenides, unlocking opportunities to explore their intriguing properties and applications.

    • Volker L. Deringer
    News & Views
    Nature Materials
    Volume: 24, P: 1152-1153

  • Although amorphous calcium carbonate represents an important biomineralization precursor, its structure has been difficult to understand. Now, amorphous calcium carbonate’s structure is shown to arise from the different bridging modes available to the calcium ions. This effective multi-well potential that drives calcium arrangements creates a geometric incompatibility between preferred Ca–Ca distances and frustrates crystallization.

    • Thomas C. Nicholas
    • Adam Edward Stones
    • Andrew L. Goodwin
    ResearchOpen Access
    Nature Chemistry
    Volume: 16, P: 36-41

  • A machine-learning-based model can be used to perform atomistic simulations of phase changes along the germanium–antimony–tellurium composition line, up to a full-size memory device model that contains half a million atoms.

    • Yuxing Zhou
    • Wei Zhang
    • Volker L. Deringer
    ResearchOpen Access
    Nature Electronics
    Volume: 6, P: 746-754

  • Atomistic simulations of phosphorus represent a challenge due to the element’s highly diverse allotropic structures. Here the authors propose a general-purpose machine-learning force field for elemental phosphorus, which can describe a broad range of relevant bulk and nanostructured allotropes.

    • Volker L. Deringer
    • Miguel A. Caro
    • Gábor Csányi
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-11

  • 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

  • As machine learning models are becoming mainstream tools for molecular and materials research, there is an urgent need to improve the nature, quality, and accessibility of atomistic data. In turn, there are opportunities for a new generation of generally applicable datasets and distillable models.

    • Chiheb Ben Mahmoud
    • John L. A. Gardner
    • Volker L. Deringer
    Comments & Opinion
    Nature Computational Science
    Volume: 4, P: 384-387

  • Helium is generally recognized as being chemically inert. A thermodynamically stable compound of helium and sodium, Na2He, has been predicted computationally and then synthesized at high pressure. It exists as an electride, where strongly localized electrons serve as anions located at the centre of Na8 cubes.

    • Xiao Dong
    • Artem R. Oganov
    • Hui-Tian Wang
    Research
    Nature Chemistry
    Volume: 9, P: 440-445

  • Amorphous materials are increasingly central components of key technologies, but their structures remain challenging to study. This Perspective highlights how recent advances in computational materials modelling and artificial intelligence are now bringing the ‘design’ of amorphous materials within reach.

    • Yuanbin Liu
    • Ata Madanchi
    • Volker L. Deringer
    Reviews
    Nature Reviews Materials
    Volume: 10, P: 228-241