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Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.

Machine intelligence has garnered immense attention owing to its ability to discover hidden patterns in abstract and high-dimensional datasets. However, its success is often limited by the fundamental bottleneck of data scarcity. In this work, we offer a universal data augmentation solution to resolve this impasse. We first discovered the hidden knowledge within the existing scarce dataset using the machine learning (ML) technique and then synthetically augmented the dataset according to its feature importance. In principle, scarce and augmented datasets should share a common statistical property. Using this property, we specifically study the scarce dataset representing the binge-drinking behavior of university students and show that our method is effective in augmenting a limited dataset with high fidelity. The current work challenges the status quo in data scarcity with rule-less-based ML, which removes the ostensible barrier that prevents the application of data-driven techniques to the data scarce clinical research.

Though plasmonic effect is making some headway in the energy harvesting realm, its fundamental charge transfer mechanism to a large extent is attributed to the hot-carrier generation at the contact interface. Herein this work attempts to elucidate the physical origin of light induced plasmo-pyroelectric enhancement based on charge density manipulation on surface state in the vicinity of the metal-ferroelectric contact interface. More importantly, by tuning the band bending, it is shown that the charge density on the surface state of a hybrid plasmo-pyroelectric (BaTiO3 -Ag) nanosystem can be manipulated and largely increased under the resonant blue light illumination (363 nm). It is also demonstrated that owing to this effect, the spatial pyroelectric activity of a hybrid plasmo-pyroelectric nanosystem governs 46% enhancement in pyroelectric coefficient. This research highlights the optically regulated charge density in plasmo-pyroelectric nanosystems, which could pave a new avenue for energy harvesting/conversion devices with distinguished advantages in wireless, photonic-controlled, localized, and dynamic stimulation.

Machine intelligence-based optimizing synthesis parameters offers exciting opportunities in various domain of materials chemistry where large and comprehensive dataset are available. Aiming to construct a “design-to-device” pipeline for battery application with small discrepancy, we adapt machine learning model based on actual experimental data from curated literatures to predict the discharge capacity of Nickel-Manganese-Cobalt (NCM) cathode material. However, missing data is an inherent problem from the results based on literature. Herein we introduce imputation techniques to fill the missing data based. To this end, we show that our model have remarkable predictive capability with the average coefficient of determinant, R2 = 0.84 for training sets. In addition, we also validate our model prediction by the experiments, which yields in less than 8% error. The present work highlights a promising data driven materials synthesis approach, which underpins nonlinear structure-properties relationship for broad range of materials development.

Fabrication of large-area, highly orderly, and high-resolution nanostructures in a cost-effective fashion prompts advances in nanotechnology. Herein, for the first time, we demonstrate a unique strategy to prepare a long-range highly regular polymer lens from photoresist nanotrenches based templates, which are obtained from underexposure. The relationship between exposure dose and the cross-sectional morphology of produced photoresist nanostructures is revealed for the first time. The polymer lens arrays are repeatedly used for rapid generation of sub-100 nm nanopatterns across centimeter-scale areas. The light focusing properties of the nanoscale polymer lens are investigated by both simulation and experiment. It is found that the geometry, size of the lens, and the exposure dose can be deployed to adjust the produced feature size, spacing, and shapes. Because the polymer lenses are derived from top-down photolithography, the nearly perfect long-range periodicity of produced nanopatterns is ensured, and the feature shapes can be flexibly designed. Because this nanolithographic strategy enables subwavelength periodical nanopatterns with controllable feature size, geometry, and composition in a cost-effective manner, it can be optimized as a viable and potent nanofabrication tool for various technological applications.

One-dimensional (1D) metallic nanocrystals constitute an important class of plasmonic materials for localization of light into subwavelength dimensions. Coupled with their intrinsic conductive properties and extended optical paths for light absorption, metallic nanowires are prevalent in light-harnessing applications. However, the transverse surface plasmon resonance (SPR) mode of traditional multiply twinned nanowires often suffers from weaker electric field enhancement due to its low degree of morphological curvature in comparison to other complex anisotropic nanocrystals. Herein, simultaneous anisotropic stellation and excavation of multiply twinned nanowires are demonstrated through a site-selective galvanic reaction for a pronounced manipulation of light-matter interaction. The introduction of longitudinal extrusions and cavitation along the nanowires leads to a significant enhancement in plasmon field with reduced quenching of localized surface plasmon resonance (LSPR). The as-synthesized multimetallic nanostartubes serve as a panchromatic plasmonic framework for incorporation of photocatalytic materials for plasmon-assisted solar fuel production.

Electron-photon coupling in metal nanostructures has raised a new trend for active plasmonic switch devices in both fundamental understanding and technological applications. However, low sensitivity switches with an on/off ratio less than 5 have restricted applications. In this work, an electrically modulated plasmonic switch based on a surface-enhanced Raman spectroscopy (SERS) system with a single fivefold stellate polyhedral gold nanoparticle (FSPAuNP) is reported. The reversible switch of the SERS signal shows high sensitivity with an on/off ratio larger than 30. Such a high on/off ratio arises primarily from the plasmonic resonance shift of the FSPAuNP with the incident laser due to the altered free electron density on the nanoparticle under an applied electrochemical potential. This highly sensitive electro-plasmonic switch may enable further development of plasmonic devices.

Noble metallic nanostructures have great potential in optical sensing application in visible and near-infrared frequencies. Their plasmonic properties can be manipulated by in situ controlling their electron densities for isolated nanostructures. However, the effect of charging remains underexplored for coupled systems. In this work, we theoretically investigated the dependence of their far-field and near-field properties on their electron densities for various coupled gold structures. With increasing electron densities, their enhancement factors increase while their plasmonic resonance peaks are blue-shifted. The resonance peak position of ellipsoid-ellipsoid dimers shows the highest sensitivity in response to the charging effects with the slope of −2.87. The surface-averaged electric field of ellipsoid monomer shows largest enhancement ratio of 1.13 with 16% excess electrons. These results can be well explained by an effective dipole moment model. In addition, we also studied the sphere-on-substrate nan...

Tailoring optical properties of artificial metamaterials, whose optical properties go beyond the limitations of conventional and naturally occurring materials, is of importance in fundamental research and has led to many important applications such as security imaging, invisible cloak, negative refraction, ultrasensitive sensing, and transformable and switchable optics. Herein, by precisely controlling the size, symmetry, and topology of alphabetical metamaterials with U, S, Y, H, U-bar, and V shapes, we have obtained highly tunable optical response covering visible-to-infrared (vis-NIR) optical frequency. In addition, we show a detailed study on the physical origin of resonance modes, plasmonic coupling, the dispersion of resonance modes, and the possibility of negative refraction. We have found that all the electronic and magnetic modes follow the dispersion of surface plasmon polaritons; thus, essentially they are electronic- and magnetic-surface-plasmon-polaritons-like (ESPP-like and MSPP-like) modes resulted from diffraction coupling between localized surface plasmon and freely propagating light. On the basis of the fill factor and formula of magnetism permeability, we predict that the alphabetical metamaterials should show the negative refraction capability in visible optical frequency. Furthermore, we have demonstrated the specific ultrasensitive surface enhanced Raman spectroscopy (SERS) sensing of monolayer molecules and femtomolar food contaminants by tuning their resonance to match the laser wavelength, or by tuning the laser wavelength to match the plasmon resonance of metamaterials. Our tunable alphabetical metamaterials provide a generic platform to study the electromagnetic properties of metamaterials and explore the novel applications in optical frequency.

Programmable photocatalysts for hydrogen evolution have been fabricated based on multi-segmented CdS-Au nanorod arrays, which exhibited high-efficiency and programmability in hydrogen evolution as the photoanodes in the photoelectrochemical cell. Multiple different components each possess unique physical and chemical properties that provide these cascade nanostructures with multiformity, programmability, and adaptability. These advantages allow these nanostructures as promising candidates for high efficient harvesting and conversion of solar energy.

New Type of Gold Nanoparticle: A new class of fivefold stellate polyhedral gold nanoparticles (FSPAuNPs) with {110} facets have been synthesized by a seed-mediated growth method without adding surfactant. The size of FSPAuNPs can be simply adjusted from nanoscale to microscale by varying the amount of seeds, which results in a shift of the surface plasmon resonance peak from the visible to the NIR range.

Engineering interfacial photo-induced charge transfer for highly synergistic photocatalysis is successfully realized based on nanobamboo array architecture. Programmable assemblies of various components and heterogeneous interfaces, and, in turn, engineering of the energy band structure along the charge transport pathways, play a critical role in generating excellent synergistic effects of multiple components for promoting photocatalytic efficiency.

Enhanced near-field at noble metal nanoparticle surfaces due to localized surface plasmon resonance (LSPR) has been researched in fields ranging from biomedical to photoelectrical applications. However, it is rarely explored on nonmetallic nanomaterials discovered in recent years, which can also support LSPR by doping-induced free charge carriers, let alone the investigation of an intricate system involving both. Here we construct a dual plasmonic hybrid nanosystem Au-Cu9S5 with well controlled interfaces to study the coupling effect of LSPR originating from the collective electron and hole oscillations. Cu9S5 LSPR is enhanced by 50% in the presence of Au, and the simulation results confirm the coupling effect and the enhanced local field as well as the optical power absorption on Cu9S5 surface. This enhanced optical absorption cross section, high photothermal transduction efficiency (37%), large light penetration depth at 1064 nm, excellent X-ray attenuation ability, and low cytotoxicity enable Au-Cu9S5 hybrids for robust photothermal therapy in the second near-infrared (NIR) window with low nanomaterial dose and laser flux, making them potential theranostic nanomaterials with X-ray CT imaging capability. This study will benefit future design and optimization of photoabsorbers and photothermal nanoheaters utilizing surface plasmon resonance enhancement phenomena for a broad range of applications.