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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.

Memristors are essential elements for hardware implementation of artificial neural networks. The key functionality of the memristors is to realize multiple non-volatile conductance states with high precision. However, the variation of device conductance limits the number of allowed states. Since actual data for neural network training inherently have a non-uniform distribution, the insufficient number of conductance states and the resultant inaccurate weight quantization may generate significant errors in the memristor-based computation. Herein, we demonstrate a multi-level memristor based on two-dimensional electron gas in a Pt/LaAlO3/SrTiO3 heterostructure. By redistributing oxygen vacancies, we precisely controlled the tunneling conductance of the device, achieving multiple conductance states (more than 27). The multi-level switching capability and the high retention performance allow us to implement a variance-aware weight quantization (VAQ), designed for improved computing accuracy. We verify that the VAQ provides greater accuracy in image classification process, as compared to conventional uniform quantization. These results provide valuable insight into developing high-precision multi-bit memristors for practical neuromorphic processors.

Electrochemical conversion of carbon dioxide (CO2) gas is a simple and cost-benefit method to produce economically valuable materials such as carbon monoxide (CO), multi-carbon (C2+) oxygenate, and hydrocarbons. In particular, ethanol (EtOH) production is attractive to be applied for conventional transport fuels. However, there has been a lack of techniques to yield predominant EtOH from CO2. Copper (Cu), as the well-known and exclusive C2+ catalyst, thermodynamically prefers the production of (C2H4) compared to EtOH. Previous studies suggested that increased concentration of carbon monoxide (CO) as the intermediate of CO2 ameliorated the EtOH selectivity.1,2 Because the solubility of CO is very little in an aqueous electrolyte solution, surface diffusion of CO has been considered to enhance the local CO concentration. It suggests that catalyst interface is crucial to increasing EtOH yield, while little knowledge was developed. We prepared porous Ag inverse opal (AgIO) frameworks with a uniform pore size of ~ 600 nm. The CO2 gas was electrochemically reduced to CO with > 90% selectivity at -1.05 V vs. RHE. In addition, the CO settled in the pore, providing an opportunity for sequential electrochemical/chemical reactions. We deposited the ultra-thin Cu layer (2~10 nm) upon the Ag surface through the pulse electrodeposition technique (PED). This catalyst, indicated as PEDCu/AgIOs, performed ~33 % Faradaic efficiency (FE) of EtOH at -1.05 V vs. RHE. Moreover, PEDCu/AgIOs showed a high oxygenates ratio relative to hydrocarbons (FEoxygenate/FEC2H4) of 2.28, while H2 evolution was significantly suppressed. These results suggested that as-prepared thin Cu film was partially segregated, and the Ag surface was exposed, forming the mixed Ag and Cu interfaces in the given electrochemical condition. This hypothesis was confirmed by negligible EtOH from the Ag-free CuIOs structure. More importantly, the resulting EtOH selectivity outperformed Cu nanoparticles (~7 nm diameter) dispersed on AgIOs (7.45 % FE) and aggregated Cu nanostructures on AgIOs (17.65 % FE) prepared by the constant current mode of electrodeposition. We, therefore, anticipated that CO spillover was promoted by the remaining and ultra-thin Cu layer. Further experiments were conducted by controlling of the thickness, pore size, and interpore size of AgIO frameworks to understand their roles. PEDCu/AgIOs with larger AgIOs thickness and smaller pore size showed higher EtOH selectivity, while the interpore size did not significantly affect the product selectivity. In the presentation, I will discuss key parameters to determine EtOH selectivity and the presumable pathway of EtOH production in details. References Ting, L. R. L.; Piqué, O.; Lim, S. Y.; Tanhaei, M.; Calle-Vallejo, F.; Yeo, B. S., Enhancing CO2 Electroreduction to Ethanol on Copper–Silver Composites by Opening an Alternative Catalytic Pathway. ACS Catal. 2020, 10 (7), 4059-4069. Gurudayal; Perone, D.; Malani, S.; Lum, Y.; Haussener, S.; Ager, J. W., Sequential Cascade Electrocatalytic Conversion of Carbon Dioxide to C-C Coupled Products. ACS Appl. Energy Mater. 2019, 2 (6), 4551-4559.

We determined a full 3D atomic structure of a dumbbell-shaped Pt nanoparticle formed by a coalescence of two nanoclusters using deep learning assisted atomic electron tomography. Formation of a double twin boundary was clearly observed at the interface, while substantial anisotropy and disorder were also found throughout the nanodumbbell. This suggests that the diffusion of interfacial atoms mainly governed the coalescence process, but other dynamic processes such as surface restructuring and plastic deformation were also involved. A full 3D strain tensor was clearly mapped, which allows direct calculation of the oxygen reduction reaction activity at the surface. Strong tensile strain was found at the protruded region of the nanodumbbell, which results in an improved catalytic activity on {100} facets. This work provides important clues regarding the coalescence mechanism and the relation between the atomic structure and catalytic property at the single-atom level.

Functional properties of nanomaterials strongly depend on their surface atomic structures, but they often become largely different from their bulk structures, exhibiting surface reconstructions and relaxations. However, most of the surface characterization methods are either limited to 2D measurements or not reaching to true 3D atomic-scale resolution, and single-atom level determination of the 3D surface atomic structure for general 3D nanomaterials still remains elusive. Here we demonstrate the measurement of 3D atomic structure at 15 pm precision using a Pt nanoparticle as a model system. Aided by a deep learning-based missing data retrieval combined with atomic electron tomography, the surface atomic structure was reliably measured. We found that and facets contribute differently to the surface strain, resulting in anisotropic strain distribution as well as compressive support boundary effect. The capability of single-atom level surface characterization will not only deepen our understanding of the functional properties of nanomaterials but also open a new door for fine tailoring of their performance., Precise determination of surface atomic structure of metallic nanoparticles is key to unlock their surface/interface properties. Here the authors introduce a neural network-assisted atomic electron tomography approach that provides a three-dimensional reconstruction of metallic nanoparticles at individual atom level.