A5046829226- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Electronic and Structural Properties of Oxides
- Advancements in Battery Materials
- Advanced Battery Materials and Technologies
- Molecular Junctions and Nanostructures
- Advanced Materials Characterization Techniques
- Phase-change materials and chalcogenides
- Computational Drug Discovery Methods
- Fuel Cells and Related Materials
- nanoparticles nucleation surface interactions
- Fashion and Cultural Textiles
- Metal and Thin Film Mechanics
- Advanced Electron Microscopy Techniques and Applications
- Metallurgical and Alloy Processes
- Inorganic Chemistry and Materials
- Cultural and Historical Studies
- Catalysis and Oxidation Reactions
- Semiconductor materials and devices
- Electrocatalysts for Energy Conversion
- Advanced Chemical Physics Studies
- Consumer Perception and Purchasing Behavior
- Copper Interconnects and Reliability
- Semiconductor materials and interfaces
- Electron and X-Ray Spectroscopy Techniques
Seoul National University
2020-2025
Solid-state electrolytes with argyrodite structures, such as Li
Ternary metal oxides are crucial components in a wide range of applications and have been extensively cataloged experimental materials databases. However, there still exist cation combinations with unknown stability structures their compounds oxide forms. In this study, we employ extensive crystal structure prediction methods, accelerated by machine-learned potentials, to investigate these untapped chemical spaces. We examine 181 ternary systems, encompassing most cations except for...
Prediction of the stable crystal structure for multinary (ternary or higher) compounds with unexplored compositions demands fast and accurate evaluation free energies in exploring vast configurational space. The machine-learning potential such as neural network (NNP) is poised to meet this requirement but a dearth information on poses challenge choosing training sets. Herein we propose constructing set from densityfunctional-theory (DFT) based dynamical trajectories liquid quenched amorphous...
Neural network potentials (NNPs) are gaining much attention as they enable fast molecular dynamics (MD) simulations for a wide range of systems while maintaining the accuracy density functional theory calculations. Since NNP is constructed by machine learning on training data, its prediction uncertainty increases drastically atomic environments deviate from points. Therefore, it essential to monitor level during MD judge soundness results. In this work, we propose an estimator based replica...
Disordering atomic structures offers a functionality hardly expected in ordered states, including phase-change memory and photonic computing, offering the potential to renovate von Neumann architecture for neuromorphic engineering with low latency. However, significant energy consumption during disordering compromises data reliability integration efficiency, which is traditionally regarded take place after melting. Here, we investigate time isochronal isochoric manners, challenging...
Recently, machine learning potentials (MLPs) have been attracting interest as an alternative to the computationally expensive density-functional theory (DFT) calculations. The data-driven approach in MLPs requires carefully curated training datasets, which define valid domain of simulations. Therefore, acquiring datasets that comprehensively span desired simulations is important. In this review, we attempt set guidelines for systematic construction according target To end, extensively...
The universal mathematical form of machine-learning potentials (MLPs) shifts the core development interatomic to collecting proper training data. Ideally, set should encompass diverse local atomic environments but conventional approach is prone sampling similar configurations repeatedly, mainly due Boltzmann statistics. As such, practitioners handpick a large pool distinct manually, stretching period significantly. Herein, we suggest novel method optimized for gathering yet relevant...
In fuel cell applications, the durability of catalysts is critical for large-scale industrial implementation. However, limited synthesis controllability and spectroscopic resolution impede a comprehensive understanding degradation mechanisms at atomic level. this study, we develop machine-learned potential (MLP) to simulate processes Pt3Co nanoparticles. The precision MLP determined be comparable that density functional theory calculations. Using off-lattice kinetic Monte Carlo simulations...
Exploring potential energy surfaces (PES) is essential for unraveling the underlying mechanisms of chemical reactions and material properties. While activation-relaxation technique (ARTn) a state-of-the-art method identifying saddle points on PES, it often faces challenges in complex landscapes, especially surfaces. In this study, we introduce iso-ARTn, an enhanced ARTn that incorporates constraints orthogonal hyperplane employs adaptive active volume. By leveraging neural network (NNP) to...
Enterprises seek to deliver images of goods and brands by various forms marketing cope with varying consumption patterns. VMD can brand image consumers effectively in terms design marketing. Various which are housed Hyundai Department Store Pangyo branch that opened August 2015 compete each other fiercely. is emerged as important tool. Therefore, research on enhancing effect required. This paper studies art collaboration used through showroom Gentle Monster, glasses analyzes technical...
Solid-state electrolytes with argyrodite structures, such as $\mathrm{Li_6PS_5Cl}$, have attracted considerable attention due to their superior safety compared liquid and higher ionic conductivity than other solid electrolytes. Although experimental efforts been made enhance by controlling the degree of disorder, underlying diffusion mechanism is not yet fully understood. Moreover, existing theoretical analyses based on ab initio MD simulations limitations in addressing various types...