Advanced search

Filter By:

Journal Check one or more journals to show results from those journals only.

Choose more journals

Article type Check one or more article types to show results from those article types only.
Subject Check one or more subjects to show results from those subjects only.
Date Choose a date option to show results from those dates only.

Custom date range

Clear all filters
Sort by:
Showing 1–26 of 26 results

  • The discovery of passivating agents for perovskite photovoltaics can be an arduous and time-consuming process. Now, a machine-learning model is reported that accelerates the selection of bifunctional pseudo-halide passivators. The identified pseudo-halide passivators were experimentally shown to enhance the performance of perovskite solar cells.

    News & Views
    Nature Materials
    Volume: 22, P: 1449-1450

  • Two computational methods — one physics-based, and the other one deep-learning based — are proposed to enable the systematic investigation of magnetic order in moiré magnets from first principles.

    • David Soriano
    News & Views
    Nature Computational Science
    Volume: 3, P: 282-284

  • A rotational and time-reversal equivariant neural network designed to represent the spin–orbital density functional theory Hamiltonian as a function of the atomic and magnetic structure enables ab initio electronic-structure calculations of magnetic superstructures. These calculations can efficiently and accurately predict subtle magnetic effects in various chemical environments.

    News & Views
    Nature Computational Science
    Volume: 3, P: 287-288

  • Dr Valentino Cooper, a Distinguished R&D Staff Member at Oak Ridge National Laboratory, talks to Nature Computational Science about his research on density functional theory and on designing high-entropy materials and piezoelectrics.

    • Fernando Chirigati
    Comments & Opinion
    Nature Computational Science
    Volume: 3, P: 116-117

  • The link between oxygen redox and structural disorder in lithium-rich layered electrodes has been challenging to unravel. A theoretical framework for the link between structural disorder, subsequent bond rearrangements and redox chemistry has been proposed, providing guidance for the materials engineering of high-capacity electrodes.

    News & Views
    Nature Sustainability
    Volume: 5, P: 647-648

  • A deep neural network method is developed to learn the density functional theory (DFT) Hamiltonian as a function of atomic structure. This approach provides a solution to the accuracy–efficiency dilemma of DFT and opens opportunities to investigate large-scale materials, such as twisted van der Waals materials.

    News & Views
    Nature Computational Science
    Volume: 2, P: 418-419

  • Charge density waves are the periodic spatial modulation of electrons in a solid. A new experiment reveals that they can originate from two different electronic bands in a prototypical transition metal dichalcogenide, NbSe2.

    • Young-Woo Son
    News & Views
    Nature Physics
    Volume: 17, P: 1284-1285

  • The work by Roberto Car and Michele Parrinello on ab initio molecular dynamics published 25 years ago has had a huge impact on fundamental science and applications in a wide range of fields.

    Editorial
    Nature Materials
    Volume: 9, P: 687

  • Electron tunnelling can be used to identify single bases in a short DNA oligomer.

    • Massimiliano Di Ventra
    News & Views
    Nature Nanotechnology
    Volume: 5, P: 828-829

  • By considering the topology of chiral crystals, a new type of massless fermion, connected with giant arc-like surface states, are predicted. Such Kramers–Weyl fermions should manifest themselves in a wide variety of chiral materials.

    • Chandra Shekhar
    News & Views
    Nature Materials
    Volume: 17, P: 953-954

  • The search for materials with colossal permittivity for use in capacitors has been met with limited success. A newly discovered co-doped titanium oxide material has an extremely high permittivity and negligible dielectric losses, and is likely to enable further scaling in electronic and energy-storage devices.

    • Christopher C. Homes
    • Thomas Vogt
    News & Views
    Nature Materials
    Volume: 12, P: 782-783

  • The ramifications of the Car–Parrinello method, a 25-year-old unified approach to computing properties of materials from first principles, have reached out well-beyond materials science.

    • Jürgen Hafner
    Comments & Opinion
    Nature Materials
    Volume: 9, P: 690-692

  • Many-electron wavefunctions face the exponential-wall problem at large electron numbers. Formulating wavefunctions with the help of cumulants effectively avoids this problem and provides a valuable starting point for electronic-structure calculations for solids.

    • Peter Fulde
    Comments & Opinion
    Nature Physics
    Volume: 12, P: 106-107