Theory and computation articles within Nature Communications

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  • Article
    | Open Access

    GeTe is a ferroelectric semiconductor with broken inversion symmetry, which leads to a large spin-orbit interaction. When doped with small amounts of manganese, it becomes magnetoelectric. Here, Krempasky et al show that the ferrimagnetic ordering of Mn-doped GeTe can be switched with unusually small currents under specific resonant conditions, orders of magnitude smaller than typical for spin-orbit torque based switching.

    • Juraj Krempaský
    • , Gunther Springholz
    •  & J. Hugo Dil

  • Article
    | Open Access

    Spin defects in semiconductors are promising for quantum technologies but understanding of defect formation processes in experiment remains incomplete. Here the authors present a computational protocol to study the formation of spin defects at the atomic scale and apply it to the divacancy defect in SiC.

    • Cunzhi Zhang
    • , Francois Gygi
    •  & Giulia Galli

  • Article
    | Open Access

    Fields of stress and plastic strain tensors in a sample under high pressures in diamond-anvil cells are important, but measuring them is difficult. Here, the authors suggest a coupled experimental-analytical-computational approach to measure them before, during, and after α−ω transformation in Zr.

    • Valery I. Levitas
    • , Achyut Dhar
    •  & K. K. Pandey

  • Article
    | Open Access

    Analysis of experimental data in condensed matter is often challenging due to system complexity and slow character of physical simulations. The authors propose a framework that combines machine learning with theoretical calculations to enable real-time analysis for electron, neutron, and x-ray spectroscopies.

    • Sathya R. Chitturi
    • , Zhurun Ji
    •  & Joshua J. Turner

  • Article
    | Open Access

    Recent work has reported puzzling results on the surface of 1T-TaS2. Based on first-principles calculations, the authors show that charge density wave order undergoes surface reconstruction, leading to modifications in the surface electronic structure, which can explain recent experiments.

    • Sung-Hoon Lee
    •  & Doohee Cho

  • Article
    | Open Access

    Strongly correlated transition metal insulators are often coloured. Understanding the underlying optical response from first-principles calculations is challenging. Now, ab initio many body Green’s function theories are shown to reproduce the colours of NiO and MnF2.

    • Swagata Acharya
    • , Dimitar Pashov
    •  & Mikhail I. Katsnelson

  • Article
    | Open Access

    Thermodynamics predicts equilibrium crystal structures and kinetics discover the pathway to form them. The authors investigate the interplay of thermodynamics and kinetics in the formation of colloidal clusters and reveal a bifurcation at an early stage of the crystallization process.

    • Chrameh Fru Mbah
    • , Junwei Wang
    •  & Michael Engel

  • Article
    | Open Access

    Machine learning methods in condensed matter physics are an emerging tool for providing powerful analytical methods. Here, the authors demonstrate that convolutional neural networks can identify nematic electronic order from STM data of twisted double-layer graphene—even in the presence of heterostrain.

    • João Augusto Sobral
    • , Stefan Obernauer
    •  & Mathias S. Scheurer

  • Perspective
    | Open Access

    Learning from human brains to build powerful computers is attractive, yet extremely challenging due to the lack of a guiding computing theory. Jaeger et al. give a perspective on a bottom-up approach to engineer unconventional computing systems, which is fundamentally different to the classical theory based on Turing machines.

    • Herbert Jaeger
    • , Beatriz Noheda
    •  & Wilfred G. van der Wiel

  • Article
    | Open Access

    Hidden local order in disordered crystals is shown to have a strong impact on electronic and phononic band structures. Local correlations within hidden-order states can open band gaps, thereby changing properties without long-range symmetry breaking.

    • Nikolaj Roth
    •  & Andrew L. Goodwin

  • Article
    | Open Access

    Level of atomic disorder in materials is critical to understanding the effect of local structure on materials properties. Here the authors present a workflow combining structure-aware graph neural networks and physics-inspired order parameter to characterize structural disorder on a per atom basis.

    • James Chapman
    • , Tim Hsu
    •  & Brandon C. Wood

  • Article
    | Open Access

    Despite the distinct electronic properties of the wide variety Cm3+ compounds that have been prepared to date, no singlecrystal structural characterization of a complex containing a Cm−C bond has been reported. Here the authors report the synthesis of a Cm complex bearing trimethylsilylcyclopentadienyl and 4,4’-bipyridine ligands with a low energy emission and identify the 4,4’-bipyridine ligand as the primary quenching agent.

    • Brian N. Long
    • , María J. Beltrán-Leíva
    •  & Thomas E. Albrecht-Schönzart

  • Article
    | Open Access

    Although Fe doping boosts the electrocatalytic performance of NiOOH materials for the oxygen evolution reaction, the underlying mechanism has been not well understood. Here, the authors reveal Fe low-spin state configuration as a main driver of this electrochemical phenomenon.

    • Zheng-Da He
    • , Rebekka Tesch
    •  & Piotr M. Kowalski

  • Article
    | Open Access

    Zeolites are porous aluminosilicate molecular sieves with uniform pores of molecular dimensions that have a wide range of applications. Here authors use machine learning to guide zeolite synthesis and predict the structure and properties of faujasite zeolites from synthesis conditions.

    • Xinyu Li
    • , He Han
    •  & Michael Tsapatsis

  • Article
    | Open Access

    In solid-state lithium metal batteries, the crystallization of Li-ions deposited at interfaces remains unclear. Here, authors use molecular dynamics simulations to reveal lithium crystallization pathways and energy barriers, guiding improved interfacial engineering and accelerated crystal growth.

    • Menghao Yang
    • , Yunsheng Liu
    •  & Yifei Mo

  • Article
    | Open Access

    It remains challenging to understand the relation between mechanical properties of glasses close to the yielding point and plastic behaviors at microscales. Wu et al. examine the plasticity using topological properties of the vibrational modes and identify a correlation between defects and plastic events.

    • Zhen Wei Wu
    • , Yixiao Chen
    •  & Limei Xu

  • Article
    | Open Access

    Recent experiments reveal undetermined crystalline phases near the melting minimum region in lithium. Here, the authors use a crystal structure search method combined with machine learning to explore the energy landscape of lithium and predict complex crystal structures.

    • Xiaoyang Wang
    • , Zhenyu Wang
    •  & Yanming Ma

  • Article
    | Open Access

    The compositional space of potential high-entropy alloys is gigantic and difficult to explore efficiently. Here, the authors use high-throughput first-principles computations to predict what elements can mix to form high-entropy alloys, understanding of the factors favoring their formation.

    • Wei Chen
    • , Antoine Hilhorst
    •  & Geoffroy Hautier

  • Article
    | Open Access

    Luminescent metal-organic frameworks are an emerging class of optical sensors capable to capture and detect toxic gases. Here, the authors report the incorporation of synergistic binding sites in MOF-808 through post-synthetic modification with copper for optical sensing of NO2 at remarkably low concentrations.

    • Isabel del Castillo-Velilla
    • , Ahmad Sousaraei
    •  & Ana E. Platero-Prats

  • Article
    | Open Access

    Lithium graphite intercalation compounds are important for developing Li-ion batteries. Here authors simulate the interaction of high energy X-rays with Li ions intercalated in graphite and show that Li ions behave in an unexpected non-Gaussian fashion, leading to increasingly chaotic behaviour as the ion concentration reduces.

    • Sasawat Jamnuch
    •  & Tod A. Pascal

  • Article
    | Open Access

    Doping is widely adopted to make organic semiconductors more conductive, yet the impact of molecular electronic properties on doping performance is still not fully understood. Armleder et al. compute host-dopant interactions and show that a short-range overscreening effect strongly affects conductivity.

    • Jonas Armleder
    • , Tobias Neumann
    •  & Artem Fediai

  • Article
    | Open Access

    As lamellar materials, smectics exhibit both liquid and solid characteristics, making them difficult to model at the mesoscale. Paget et al. propose a complex tensor order parameter that reflects the smectic symmetries, capable of describing complex defects including dislocations and disclinations.

    • Jack Paget
    • , Marco G. Mazza
    •  & Tyler N. Shendruk

  • Article
    | Open Access

    The quantum properties of hydrogen atoms in zeolite-catalyzed reactions are generally neglected due to high computational costs. Here, the authors leverage machine learning to derive accurate quantum kinetics for proton transfer reactions in heterogeneous catalysis.

    • Massimo Bocus
    • , Ruben Goeminne
    •  & Veronique Van Speybroeck

  • Article
    | Open Access

    In this work, the authors implement a crystalline rank-2 chiral modes by employing non-Hermitian dynamics. They showed the momentum-resolved dynamics and non-Hermitian skin effect associated with the rank-2 chirality both theoretically and experimentally.

    • Penghao Zhu
    • , Xiao-Qi Sun
    •  & Gaurav Bahl

  • Article
    | Open Access

    The paper presents a method that allows scaling machine learning interatomic potentials to extremely large systems, while at the same time retaining the remarkable accuracy and learning efficiency of deep equivariant models. This is obtained with an E(3)- equivariant neural network architecture that combines the high accuracy of equivariant neural networks with the scalability of local methods.

    • Albert Musaelian
    • , Simon Batzner
    •  & Boris Kozinsky

  • Article
    | Open Access

    The avalanche of publications challenges the norm that researchers extract knowledge from literature to design materials. Here the authors present a text-mining method that is implemented based on the abstracts of 6.4 million papers to enable the design of new high entropy alloys.

    • Zongrui Pei
    • , Junqi Yin
    •  & Dierk Raabe

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