Theory and computation articles within Communications Materials

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  • Article
    | Open Access

    Irradiation-induced void swelling is known to be higher in metals with an fcc structure compared to bcc, though the reason behind this is unclear. Here, by combining simulations and STEM imaging, stacking fault tetrahedra are found to be the cause of a high swelling rate in fcc copper.

    • Ziang Yu
    • , Yan-Ru Lin
    •  & Haixuan Xu

  • Article
    | Open Access

    Machine learning models can predict the formation energy of compounds with high accuracy and efficiency. Here, the authors develop a deep convolutional network for high-throughput materials screening based on visual image representations of crystals instead of conventional graph structures, providing an alternative state-of-the-art approach that benefits from the most recent advances in image recognition architectures.

    • Ali Davariashtiyani
    •  & Sara Kadkhodaei

  • Article
    | Open Access

    Hafnia ferroelectrics hold exciting technological potential, but the variety of phases and unconventional properties found in these materials make them extremely challenging to describe theoretically. Here, an approach based on an original reference phase provides a unifying picture to understand the multiple low-energy polymorphs of hafnia.

    • Hugo Aramberri
    •  & Jorge Íñiguez

  • Article
    | Open Access

    The ability of a structure to reliably change its shape is key to the function of various organisms in nature, as well as for applications such as implants and robotics. Here, a methodology is shown to predict shape-morphing in kinematic structures, based on geometrical multibody design of connecting elements and joints.

    • Pier H. de Jong
    • , A. L. Schwab
    •  & Amir A. Zadpoor

  • Article
    | Open Access

    High-throughput computational screening accelerates the search for promising metal-organic frameworks but often neglects stability. Here, four stability metrics are integrated with high-throughput computational screening to identify top-performing metal-organic frameworks for carbon dioxide capture.

    • Saad Aldin Mohamed
    • , Daohui Zhao
    •  & Jianwen Jiang

  • Article
    | Open Access

    Predicting phonon properties is essential for identifying thermally efficient materials. Here, an indirect bottom-up machine learning approach is able to predict comprehensive phonon properties of ~80,000 cubic crystals spanning 63 elements, thereby overcoming the computational burden of first-principles calculations.

    • Alejandro Rodriguez
    • , Changpeng Lin
    •  & Ming Hu

  • Article
    | Open Access

    Using machine learning to construct interatomic potentials when materials are not in their electronic ground state is challenging. Here, a neural network interatomic potential is constructed for laser-excited silicon, which extends first-principles accuracy to ultra-large length and time scales.

    • Pascal Plettenberg
    • , Bernd Bauerhenne
    •  & Martin E. Garcia

  • Article
    | Open Access

    Catch bonds exist in some protein-ligand complexes and are of interest for their increased lifetime under greater mechanical force. Here, a mathematical model for nanoparticles tethered with macromolecules shows catch-bond behavior, which may be useful for designing synthetic materials.

    • Kerim C. Dansuk
    • , Subhadeep Pal
    •  & Sinan Keten

  • Article
    | Open Access

    The melt growth of ice - crystallization from supercooled water - has complex anisotropic kinetics, closely related to the rich variety of snowflake crystals. Here, molecular dynamics simulations shed light on its microscopic mechanism, identifying a layer of ultralow density water at the growth interface.

    • Kenji Mochizuki
    • , Ken-ichiro Murata
    •  & Xuan Zhang

  • Article
    | Open Access

    Defect engineering and doping of semiconductors by ion irradiation are essential in large-scale integration of electronic devices. Here, intense ion pulses from a laser-accelerator, with flux levels up to 1022 ions cm-2 s-1, are used to induce and optimize silicon color centers and photon emitters in the telecom band.

    • Walid Redjem
    • , Ariel J. Amsellem
    •  & Thomas Schenkel

  • Article
    | Open Access

    The intriguing physics of correlated flat bands in moiré superlattices can be mimicked, in classical physics, by twisted acoustic plates with periodic holes. Here, the authors derive a combined analytical and numerical approach that provides computational advantage in band engineering of holey bilayer plates.

    • María Rosendo López
    • , Zhiwang Zhang
    •  & Johan Christensen

  • Article
    | Open Access

    Biodegradable polyhydroxyalkanoates are promising replacements for non-degradable plastics. Here, neural network property predictors are applied to a search space of approximately 1.4 million candidates, identifying 14 polyhydroxyalkanoates that could replace widely used petroleum-based plastics.

    • Christopher Kuenneth
    • , Jessica Lalonde
    •  & Ghanshyam Pilania

  • Review Article
    | Open Access

    Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. This Review discusses state-of-the-art architectures and applications of graph neural networks in materials science and chemistry, indicating a possible road-map for their further development.

    • Patrick Reiser
    • , Marlen Neubert
    •  & Pascal Friederich

  • Article
    | Open Access

    Grain boundary self-diffusion mechanisms are not well understood, especially at intermediate temperatures most relevant to engineering applications. Here, molecular dynamics simulations at intermediate temperatures reveal strongly intermittent grain boundary diffusion behavior and finite size effects arising from thermally activated point defect avalanches

    • Ian Chesser
    •  & Yuri Mishin

  • Article
    | Open Access

    In data-driven approaches for materials discovery, it is essential to account for phase stability when predicting synthesizability. Here, by combining density functional theory calculations and machine learning, the authors predict the synthesizability of unreported half-Heusler compositions.

    • Andrew Lee
    • , Suchismita Sarker
    •  & Christopher Wolverton

  • Article
    | Open Access

    Short-range chemical ordering has emerged as a key feature for controlling the properties of high-entropy alloys. Here, ab initio calculations reveal that two types of short-range ordering exist in Si–Ge–Sn medium-entropy alloys, suggesting that multiple types of ordering could exist in a single alloy.

    • Xiaochen Jin
    • , Shunda Chen
    •  & Tianshu Li

  • Perspective
    | Open Access

    Machine learning is an increasingly important tool for materials science. Here, the authors suggest that its contextual use, including careful assessment of resources and bias, judicious model selection, and an understanding of its limitations, will help researchers to expedite scientific discovery.

    • Naohiro Fujinuma
    • , Brian DeCost
    •  & Samuel E. Lofland

  • Article
    | Open Access

    Understanding the effects of fast neutrons on high-temperature superconductors is important for their application in fusion reactors. Here, a combined experimental and theoretical study reveals that ion irradiation disrupts superconductivity by introducing defects within the copper-oxygen planes.

    • Rebecca J. Nicholls
    • , Sofia Diaz-Moreno
    •  & Susannah C. Speller

  • Article
    | Open Access

    The 3D stiffness of a self-folded metamaterial structure is limited by the low stiffness required by the folding process. Here, the stiffness limits of self-folding bilayers are theoretically established by a nonlinear model and experimentally validated on polymer-metal composites, providing the optimal combinations of geometrical and mechanical properties of folded constructs.

    • Teunis van Manen
    • , Vahid Moosabeiki Dehabadi
    •  & Amir A. Zadpoor

  • Article
    | Open Access

    Rare-earth hexaborides are of interest for their pressure-induced phase transformations, but further understanding is needed regarding their failure mechanisms. Here, nanoindentation of EuB6 causes dislocation-mediated shear band formation, driven by the breaking of boron-boron bonds.

    • Rajamallu Karre
    • , Yidi Shen
    •  & Kolan Madhav Reddy

  • Article
    | Open Access

    Metal halide to perovskite phase conversion is a facile approach for synthesizing high-quality perovskite semiconductors for optoelectronic applications. Here, these reactions are investigated at the nanoscale via in-situ x-ray scattering, revealing links between reaction kinetics, structure and composition.

    • Neha Arora
    • , Alessandro Greco
    •  & M. Ibrahim Dar

  • Article
    | Open Access

    The BaSnO3 perovskite is promising for electronic applications due to its transparency and high room-temperature mobility, but its effective masses, band gaps, and absorption edge are still controversial. Here, a combined theoretical and experimental study provides a consistent picture of its electronic structure and optical excitations.

    • Wahib Aggoune
    • , Alberto Eljarrat
    •  & Claudia Draxl

  • Article
    | Open Access

    Chemical vapor deposition of diamond typically results in the faster growth of the (110) facet, but achieving large-area and high-quality surfaces is challenging and requires post-growth processing. Here, the authors present a systematic characterization of the structure and stability of oxygen-terminated diamond (110) surfaces.

    • Shayantan Chaudhuri
    • , Samuel J. Hall
    •  & Reinhard J. Maurer

  • Article
    | Open Access

    Origami is a promising source of inspiration in designing foldable structures and reconfigurable metamaterials. Here, building on exact folding kinematic conditions, an algorithmic design of rigidly-foldable origami structures is presented, allowing the engineering of metamaterials with arbitrary complex crease patterns.

    • Andreas Walker
    •  & Tino Stankovic

  • Article
    | Open Access

    Predicting the synthesizability of unknown crystals is important for accelerating materials discovery. Here, the synthesizability of crystals with any given composition and structure can be predicted by a deep learning model that maps crystals onto color-coded 3D images processed by convolutional neural networks.

    • Ali Davariashtiyani
    • , Zahra Kadkhodaie
    •  & Sara Kadkhodaei

  • Article
    | Open Access

    There is an ongoing need to increase the operating temperature of jet engines, requiring new high-temperature materials. Here, local phase transformations at superlattice stacking faults contribute to a three times improvement in creep strength in a Ni-based superalloy.

    • Timothy M. Smith
    • , Nikolai A. Zarkevich
    •  & Michael J. Mills

  • Review Article
    | Open Access

    Quantum materials host many exotic properties, which might be utilized for new electronic devices. Here, artificial intelligence for the discovery of quantum materials is discussed, covering both materials and property prediction, and high-throughput synthesis.

    • Valentin Stanev
    • , Kamal Choudhary
    •  & Ichiro Takeuchi

  • Article
    | Open Access

    Experiments and simulations can reveal energetic barriers during atomic-scale growth but are time-consuming. Here, machine learning is applied to single images from kinetic Monte Carlo simulations of sub-monolayer film growth, allowing diffusion barriers and binding energies to be accurately determined.

    • Thomas Martynec
    • , Christos Karapanagiotis
    •  & Stefan Kowarik

  • Article
    | Open Access

    Theoretical treatments of electrocaloric effects, interesting for their potential use in refrigeration, are mostly case specific. Here, a perturbative approach provides a unified physical understanding of the normal and inverse electrocaloric response occurring in ferroelectrics and antiferroelectrics.

    • Mónica Graf
    •  & Jorge Íñiguez

  • Article
    | Open Access

    Doping organic semiconductors can substantially increase their conductivity, yet the effect on charge transport is not fully understood. Here, an approach based on percolation theory determines the activation energy of conductivity and the Seebeck energy for archetypal small molecule materials.

    • Andreas Hofacker

  • Article
    | Open Access

    Materials with a spinel structure are used in various applications, including in the nuclear industry and as dielectrics. Here, first principle calculations and Monte Carlo simulations predict that an ordered double spinel structure is stable, supported by preliminary experimental data.

    • Ghanshyam Pilania
    • , Vancho Kocevski
    •  & Blas P. Uberuaga

  • Article
    | Open Access

    Multiferroics that are both ferroelectric and ferromagnetic are highly desirable for technological applications but extremely rare. Here, signatures of a ferroelectric phase transition, supported by theoretical calculations, are observed in ferromagnetic EuO under a large epitaxial strain of 6.4%.

    • Veronica Goian
    • , Rainer Held
    •  & Stanislav Kamba

  • Article
    | Open Access

    Transition metal nitride coatings exhibit high hardness, but typically lack ductility and are therefore prone to failure. Here, the effect of bilayer thickness on the mechanical properties of MoN-TaN superlattices is investigated, leading to coatings with high fracture toughness.

    • Rainer Hahn
    • , Nikola Koutná
    •  & Paul H. Mayrhofer

  • Article
    | Open Access

    Uranium dioxide is commonly doped with chromium to improve its performance as a nuclear fuel. Here, with the aid of ab initio simulations and re-evaluation of experimental data, the oxidation state of chromium in the uranium dioxide lattice is identified as +2, not the widely believed +3.

    • Mengli Sun
    • , Joshua Stackhouse
    •  & Piotr M. Kowalski

  • Article
    | Open Access

    Ring polymers are known to display a variety of unusual dynamical behavior due to their topology. Here, simulations reveal that ring polymers under shear flow exhibit a swelling of the ring, causing them to behave as non-Brownian particles.

    • Maximilian Liebetreu
    •  & Christos N. Likos

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