A5001151989- Machine Learning in Materials Science
- Electrocatalysts for Energy Conversion
- Fuel Cells and Related Materials
- Advanced battery technologies research
- Catalysis and Oxidation Reactions
- CO2 Reduction Techniques and Catalysts
- Advanced Photocatalysis Techniques
- Topic Modeling
- Surface Chemistry and Catalysis
- Electrochemical Analysis and Applications
- Advanced Battery Materials and Technologies
- Advanced Graph Neural Networks
- Computational Drug Discovery Methods
- X-ray Diffraction in Crystallography
- Chalcogenide Semiconductor Thin Films
- Protein Structure and Dynamics
- Advancements in Battery Materials
- Ionic liquids properties and applications
- Copper-based nanomaterials and applications
- Advanced oxidation water treatment
- Advanced Thermoelectric Materials and Devices
- Environmental remediation with nanomaterials
- Conducting polymers and applications
- Data Quality and Management
- Inorganic Chemistry and Materials
Sogang University
2021-2025
Abstract Single‐atom M‒N‒C catalysts have attracted tremendous attention for their application to electrocatalysis. Nitrogen‐coordinated mononuclear metal moieties (MN x moities) are bio‐inspired active sites that analogous various metal‐porphyrin cofactors. Given the functions of cofactors highly dependent on local coordination environments around site, engineering MN in heterogeneous would provide an additional degree freedom boosting electrocatalytic activity. This work presents a...
Abstract The electrochemical carbon dioxide reduction reaction (CO 2 RR) is an attractive approach for mitigating CO emissions and generating value-added products. Consequently, discovery of promising RR catalysts has become a crucial task, machine learning (ML) been utilized to accelerate catalyst discovery. However, current ML approaches are limited exploring narrow chemical spaces provide only fragmentary catalytic activity, even though produces various chemicals. Here, by merging...
Abstract The reconstructed surface structure of Co‐based spinel oxides serves as the active site for oxygen evolution reaction (OER). However, structural complexity and dynamics during OER hinder understanding reconstruction mechanism electronic site. In this study, Co 3 O 4 @(CoFeV) nanocube (CoFeV) is reported, a (001) facet‐defined oxide comprising Co, Fe, V deposited on template to exclude facet‐dependent factors. Introducing highly dissoluble cations accelerates process enhance...
Se-based nanoalloys as an emerging class of metal chalcogenide with tunable crystalline structure, component distribution, and electronic structure have attracted considerable interest in renewable energy conversion utilization. In this Letter, we report a series nanosized M-Se catalysts (M = Cu, Ni, Co) prepared from laser ablation method screen their electrocatalytic performance for onsite H2O2 generation selective oxygen reduction reaction (ORR) alkaline media. A flexible control on...
The electrochemical reduction of CO2 (CO2RR) using renewable electricity has the potential to reduce atmospheric levels while producing valuable chemicals and fuels. However, practical implementation this technology is limited by activity, selectivity, stability catalyst materials. In study, we employ high-throughput density functional theory (DFT) calculations screen ∼800 transition metal nitrides identify catalysts for CO2RR. activity screened materials were thoroughly evaluated via...
Abstract Understanding selectivity trends is a crucial hurdle in the developing innovative catalysts for generating hydrogen peroxide through two‐electron oxygen reduction reaction (2e‐ORR). The identification of patterns has been made more accessible introduction newly developed descriptor derived from thermodynamics, denoted as ΔΔG introduced Chem Catal . 2023 , 3(3) 100568. To validate suitability this parameter 2e‐ORR selectivity, we utilize an extensive library 155 binary alloys. We...
Alkaline water electrolysis (AWE), a predominant technology for large-scale industrial hydrogen production, faces limitations in commercialization owing to the inadequate catalytic activity and stability of oxygen evolution reaction (OER) electrocatalysts. This study introduces NiFeAl self-supported electrode characterized by high OER outlines rational design strategy NiFe (oxy)hydroxide-based electrodes. The introduction Al, ternary dopant with relatively low electronegativity small ionic...
Precise electrochemical synthesis of commodity chemicals and fuels from CO 2 building blocks provides a promising route to close the anthropogenic carbon cycle, in which renewable but intermittent electricity could be stored within greenhouse gas molecules. Here, we report state-of-the-art -to-HCOOH valorization performance over multiscale optimized Cu–Bi cathodic architecture, delivering formate Faradaic efficiency exceeding 95% an aqueous electrolyzer, C-basis HCOOH purity above 99.8%...
Abstract As an oxidant, the ferryl‐oxo complex (Fe IV ═O) offers excellent reactivity and selectivity for degrading recalcitrant organic contaminants. However, enhancing Fe ═O generation on heterogeneous surfaces remains challenging because underlying formation mechanism is poorly understood. This study introduces edge defects onto a single‐atom catalyst (FeNC‐edge) to promote via peroxymonosulfate (PMS) activation. In presence of PMS, FeNC‐edge at low dose (20 mg L −1 , equivalent 0.14 Fe)...
Abstract Electrochemically generating hydrogen peroxide (H 2 O ) from oxygen offers a more sustainable and cost‐effective alternative to conventional anthraquinone process. In alkaline conditions, H is unstable as HO − , in neutral electrolytes, alkali cation crossover causes system instability. Producing acidic electrolytes ensures enhanced stability efficiency. However, the reduction reaction mechanism dominated by inner‐sphere electron transfer pathway, requiring careful consideration of...
Discovery of novel and promising materials is a critical challenge in the field chemistry material science, traditionally approached through methodologies ranging from trial-and-error to machine-learning-driven inverse design. Recent studies suggest that transformer-based language models can be utilized as generative expand chemical space explore with desired properties. In this work, we introduce catalyst pretrained transformer (CatGPT), trained generate string representations inorganic...
Discovering new solid electrolytes (SEs) is essential to achieving higher safety and better energy density for all-solid-state lithium batteries. In this work, we report machine learning (ML)-assisted high-throughput virtual screening (HTVS) results identify SE materials. This approach expands the chemical space explore by substituting elements of prototype structures accelerates an evaluation properties applying various ML models. The in a few candidate materials, which are validated...
Abstract Understanding selectivity trends is a crucial hurdle in the developing innovative catalysts for generating hydrogen peroxide through two‐electron oxygen reduction reaction (2e‐ORR). The identification of patterns has been made more accessible introduction newly developed descriptor derived from thermodynamics, denoted as ΔΔG introduced Chem Catal . 2023 , 3(3) 100568. To validate suitability this parameter 2e‐ORR selectivity, we utilize an extensive library 155 binary alloys. We...
Exposing facet and surface strain are critical factors affecting catalytic performance but unraveling the composition-dependent activity on specific facets under strain-controlled environment is still challenging due to synthetic difficulties. Herein, we achieved a (001) facet-defined Co-Mn spinel oxide with different compositions using epitaxial growth Co3O4 nanocube template. We adopted composition gradient synthesis relieve layer by layer, minimizing effect activity. In this system,...
To realize a renewable and sustainable energy cycle, there has been lot of effort put into discovering catalysts with desired properties from large chemical space. achieve this goal, several screening strategies have proposed, most which require validation thermodynamic stability synthesizability candidate materials via computationally intensive quantum chemistry or solid-state physics calculations. This problem can be overcome by reducing the number calculations through machine learning...
To discover new catalysts using density functional theory (DFT) calculations, binding energies of reaction intermediates are considered as descriptors to predict catalytic activities. Recently, machine learning methods have been developed reduce the number computationally intensive DFT calculations for a high-throughput screening. These require several steps such bulk structure optimization, surface modeling, and active site identification, which could be time-consuming candidate materials...
We developed Electronic Structure Network (ESNet) to predict formation energies using density of states extracted from initial structures. ESNet outperformed previously reported models that used other input features and architectures.
Discovery of novel and promising materials is a critical challenge in the field chemistry material science, traditionally approached through methodologies ranging from trial-and-error to machine learning-driven inverse design. Recent studies suggest that transformer-based language models can be utilized as generative expand chemical space explore with desired properties. In this work, we introduce Catalyst Generative Pretrained Transformer (CatGPT), trained generate string representations...
Green hydrogen production from water splitting is a feasible way for intermittent renewable energy storage and utilization, where the exploration scale-up preparation of high-performance anodic oxygen evolution electrocatalysts are critical prerequisites its industrial-level applications. Herein, chemical bath deposition FeNi
Multi-Metal Oxide Nanoparticles Multi-metal oxide (MMO) nanomaterials have significant potential to facilitate various demanding (electro)catalytic reactions, but its intrinsic complexity hinders the in-depth understanding of origin catalytic activity. In article number 2110857, Seoin Back, Seung-Ho Yu, Yung-Eun Sung, Taeghwan Hyeon, and co-workers report structural uniform-sized spinel-type MMO nanoparticles, which boosts electrocatalytic oxygen reduction reaction (ORR) Physicochemical...
For CO* and H* binding energy prediction, we develop new representation of catalyst surface which split into three types site, first nearest neighbor adsorbates second in same layer sublayer. From this machine learning regression model, achieve reasonable accuracy (0.120 eV for 0.105 H*) with quick training (~200 sec using CPU). Because our does not require density functional calculation atomic structure modelling, it can predict energies possible active motifs without time-consuming steps.
Towards a sustainable energy future, it is essential to develop new catalysts with improved properties for key catalytic systems such as Haber-Bosch process, water electrolysis and fuel cell. Unfortunately, the current state-of-the-art still suffer from high cost of noble metals, insufficient activity long-term stability. Furthermore, strategy relies on “trial-and-error” method, which could be time-consuming inefficient. To tackle this challenge, atomic-level simulations have demonstrated...
To realize renewable and sustainable energy cycle, there has been a lot of effort put into discovering catalysts with desired properties from large chemical space. achieve this goal, several screening strategies have proposed, most which require validation thermodynamic stability synthesizability candidate materials via computationally intensive quantum chemistry or solid-state physics calculations. This problem can be overcome by reducing the number calculations through machine learning...