Mathematics and computing articles within Communications Materials

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  • Article
    | Open Access

    Materials language and processing with large language models provide an automated approach for text classification. Here, a generative pretrained transformer (GPT) approach is reported to provide a simple architecture for text classification, including identifying incorrectly annotated data and for manual labelling.

    • Jaewoong Choi
    •  & Byungju Lee

  • Article
    | Open Access

    Space-coiling acoustic metamaterials, where sound travels through labyrinthine geometries, are interesting for their high energy transmission and broad modulation characteristics. Here, the authors demonstrate an active approach to acoustic metamaterial reconfiguration based on dynamic space-coiling unit cells and soft robotic-inspired actuators.

    • Christabel Choi
    • , Shubhi Bansal
    •  & Sriram Subramanian

  • Article
    | Open Access

    Copper sulfidation in the rubber/brass interface of tires during aging is still not well understood. Here, the 3D spatial location and chemical states of copper species in a rubber/brass composite are visualized and tracked by 3D X-ray spectroimaging with data-driven machine learning analysis.

    • Hirosuke Matsui
    • , Yuta Muramoto
    •  & Mizuki Tada

  • Article
    | Open Access

    Brain-inspired neuromorphic computing is a key technology for processing an ever-growing amount of data. Here, an artificial synapse with dual resistance modulation mechanisms is demonstrated, achieving a dynamic range of 60, an endurance exceeding 1010 cycles, and more than 10 years of retention.

    • Mattia Halter
    • , Laura Bégon-Lours
    •  & Bert Jan Offrein

  • 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

    Designing and understanding quantum materials requires continuous feedback between experimental observations and theoretical modelling. Here, a machine learning scheme integrates experiments with theory and modelling on experimental timescales for extracting material parameters and properties of Dy2Ti2O7 spin-ice under pressure.

    • Anjana Samarakoon
    • , D. Alan Tennant
    •  & Santiago A. Grigera

  • 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

    Recent advances in scanning probe-based tomographic imaging have greatly improved spatial resolution, but systematic and random errors are a serious impediment to reliable data extraction. Here, a combined optimization and alignment algorithm provides a scalable approach to error-correcting reconstruction of large datasets.

    • Sajid Ali
    • , Matthew Otten
    •  & Z. W. Di

  • Article
    | Open Access

    Multi-material 3D printing techniques are now enabling the rational design of metamaterials with both complex geometries and multiple materials compositions. Here, deep-learning methods are used to identify, among planar network structures, the rare designs that yield very unusual and desirable combinations of materials properties.

    • Helda Pahlavani
    • , Muhamad Amani
    •  & Amir A. Zadpoor

  • 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

    Topological data analysis is an important framework for quantifying the structural and morphological features of soft materials. Here, structural heterogeneity is introduced as a quantitative measure of non-equilibrium mesoscopic order in soft matter and used to track the time-evolution of liquid crystal phase transitions.

    • Ingrid Membrillo Solis
    • , Tetiana Orlova
    •  & Malgosia Kaczmarek

  • 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

    Finding materials with large magnetization is highly desirable for technological applications. Here, a machine learning autonomous search and automated combinatorial synthesis reveal that multi-element alloys with Ir and Pt impurities have a magnetization exceeding the Slater-Pauling limit of Fe75Co25.

    • Yuma Iwasaki
    • , Ryohto Sawada
    •  & Masahiko Ishida

  • Article
    | Open Access

    Traditionally, machine learning for materials science is based on database-specific models and is limited in the number of predictable parameters. Here, a versatile graph-based neural network can integrate multiple data sources, allowing the prediction of more than 40 parameters simultaneously.

    • Kan Hatakeyama-Sato
    •  & Kenichi Oyaizu

  • 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

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