Towards a New Phase in Materials Science with Hyperordered Structures

The material properties that dictate their functionality and performance are largely determined by the details in their structures. To discover new materials, researchers explore the various transitions in the material’s structure, namely intermediate phases between perfectly regular and random structures.

Termed “disorder in order” or “order within disorder,” these interphases occur in a variety of materials, including dielectrics, glasses, biomaterials and superconductors, and are known as “hyperordered structures.” Composed of a unique combination of elements, dopants, vacancies, and voids, hyperordered structures are a source of high functionality of materials for the development of next-generation devices.

A recent Special Topics edition of the Journal of the Physical Society of Japan presents articles covering the latest developments on this front.

Taniguchi et al. reviewed hyperordered structures in ferroelectric and dielectric materials to create materials with highly ordered structures along with improved permittivity, breakdown strength, and energy storage efficiency.

Pilgrim et al. studied materials that emit bright white light and form an amorphous solid when exposed to infrared radiation. Such materials can be used as infrared-driven second harmonic generators for developing novel efficient light sources.

Kimura et al. used x-ray fluorescence holography to uncover hyperordered structure embedded in materials and provided useful information for the creation of innovative structural and functional materials.

Yokoya et al. assessed the application of photoelectron holography and photoelectron diffraction methods in observing the three-dimensional local atomic structure surrounding a dopant atom. Doping is crucial for modifying a material's properties, and these techniques offer insight into enhancing the functionalities of doped materials.

Fedchenko et al. used time-of-flight momentum microscopy with hard-X-ray photoelectron diffraction for the structural analysis of solids to find doping sites in semiconductors.

Tanaka analyzed how ferredoxin, a metal-containing protein in plants, regulates electron distribution between various enzymes during photosynthesis. This finding can improve artificial photosynthesis technology, including hydrogen production.

Ishikita et al. described how water molecules spit into oxygen, electrons, and protons in the photosystem II protein environment in plants. Their work could be useful for developing artificial catalysts for the production of solar fuels like hydrogen and hydrocarbons.

Masuno studied the structural features of glasses produced using a levitation technique where the melted sample was cooled while suspended in air. The technique changed the properties of the glass, resulting in a high-quality material with properties like high refractive index and hardness that can have applications in smartphone cover glass and optical lenses.

Weber et al. reviewed various in situ x-ray techniques for investigating materials under extreme conditions and accessing metastable states. Such methods, termed “containerless” techniques, can be used to investigate supercooled liquids, opening doors to new type of glasses.

Benmore et al. developed a machine learning model based on x-ray diffraction measurements to predict material properties including structure, dynamics, and physical behavior in conditions not achievable through experiments.

Nakayama et al. used a multi-scale simulation approach to visualize the arrangements of cations and vacancies in lithium-doped lanthanum niobite, a candidate solid-state electrolyte material for lithium-ion batteries that can help us develop safer and more reliable rechargeable batteries.

Nakata et al. used a large-scale density functional theory calculation method to investigate atomic and electronic structures of materials with large and complex structures. Such methods can help us gain fundamental knowledge of semiconductors, lithium-ion batteries, and glassy materials.

Finally, Obayashi et al. developed an emerging data analysis method called persistent homology that uses topology to evaluate disordered structures of materials. Such a technique can help scientists understand the physical properties of granular materials, glass, and polymers, and guide the development of liquid crystals, emulsions, and colloidal gels.

In summary, these papers showcase the different experimental and computational techniques used to identify hyperordered structures in a wide variety of materials. Understanding such structures better could open doors to improved semiconductors, solar panels, and batteries along with advancements in high-speed communication and a sustainable, zero-carbon society.