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| 1 | Deep-learning-based inverse design model for intelligent discovery of organic molecules显示文摘The discovery of high-performance functional materials is crucial for overcoming technical issues in modern industries.Extensive efforts have been devoted toward accelerating and facilitating this process,not only experimentally but also from the viewpoint of materials design.Recently,machine learning has attracted considerable attention,as it can provide rational guidelines for efficient material exploration without time-consuming iterations or prior human knowledge.In this regard,here we develop an inverse design model based on a deep encoder-decoder architecture for targeted molecular design.Inspired by neural machine language translation,the deep neural network encoder extracts hidden features between molecular structures and their material properties,while the recurrent neural network decoder reconstructs the extracted features into new molecular structures having the target properties.In material design tasks,the proposed fully data-driven methodology successfully learned design rules from the given databases and generated promising light-absorbing molecules and host materials for a phosphorescent organic light-emitting diode by creating new ligands and combinatorial rules. | Kyungdoc Kim Seokho Kang Jiho Yoo Youngchun Kwon Youngmin Nam Dongseon Lee Inkoo Kim Youn-Suk Choi Yongsik Jung Sangmo Kim Won-Joon Son Jhunmo Son Hyo Sug Lee Sunghan Kim Jaikwang Shin Sungwoo Hwang | 2018 | npj Computational Materials2018，，1： | 4 |
| 2 | Shear load transfer characteristics of shafts in weathered rocks显示文摘 | SOOIL KIM SANG SEOM JEONG SUNGHAN CHO | 1999 | Journal of Geotechnical Engineering1999，125，11： | 1 |
| 3 | Automatic spike detection based on adaptive template matching for extracellular neural recordings 显示文摘 | Sunghan Kim James McNames | 2007 | Journal of Neuroscience Methods2007，165，2： | 1 |
| 4 | Shear load transfer characters of drilled shafts in weathered rocks 显示文摘 | Sooil Kim Sangseom Jeong Sunghan Cho | 1999 | Journal of Geotechnical and Geo environmental Engineering1999，，11： | 1 |
| 5 | Morphology and electric potential-induced mechanical behavior of metallic porous nanostructures显示文摘Understanding mechanical behaviors influenced by electric potential and tribological contacts is important for verifying the robustness and reliability of applications based on metallic porous nanostructures in electrical stimulations.In this work,nickel-based metallic porous nanostructures were studied to characterize their mechanical properties and morphologically dependent contact areas during application of an electric potential using a nanoindenter.W e observed that the indentation moduli of nickel-based metallic porous nanostructures were altered by pore size and application of electric potential.In addition,the structural aspects of the surface morphology of nickel-based porous nanostructures had a critical effect on the determination of contact area.W e suggest that the relation between electric potential and the mechanical behaviors of metallic porous nanostructures can be crucial for building mechanically robust functional devices,which are influenced by electric potential.The morphological shape characteristics of metallic porous nanostructures can be alternative decisive factors for manipulation of tribological performance through regulation of contact area. | Sunghan KIM Andreas APOLYCARPOU Hong LIANG | 2020 | Friction2020，8，3： | 1 |
| 6 | Shear load transfer characteristics of drilled shafts in weathered rocks 显示文摘 | Sooil Kim Sangseom Jeong Sunghan Cho | 1999 | Geotechnical and Geoenvironmental Engineering1999，125，11： | 1 |
| 7 | Shear load transfer characteristics of shafts in weathered rocks 显示文摘 | SOOIL KIM SANGSEOM JEONG SUNGHAN CHO | 1999 | Journal of geotechnical engineering1999，125，11： | 1 |
| 8 | Tracking tremor frequency in spike trainsusing the extended Kalman smoother显示文摘 | Sunghan Kim Mc Names J | 2006 | IEEE Transactions on Biomedical Engineering2006，53，8： | 1 |
| 9 | Mechanical properties of graphene oxide?silk fibroin bionanofilms via nanoindentation experiments and finite element analysis显示文摘Understanding the mechanical properties of bionanofilms is important in terms of identifying their durability.The primary focus of this study is to examine the effect of water vapor annealed silk fibroin on the indentation modulus and hardness of graphene oxide-silk fibroin(GO-SF)bionanofilms through nanoindentation experiments and finite element analysis(FEA).The GO-SF bionanofilms were fabricated using the layer-by-layer technique.The water vapor annealing process was employed to enhance the interfacial properties between the GO and SF layers,and the mechanical properties of the GO-SF bionanofilms were found to be affected by this process.By employing water vapor annealing,the indentation modulus and hardness of the GO-SF bionanofilms can be improved.Furthermore,the FEA models of the GO-SF bionanofilms were developed to simulate the details of the mechanical behaviors of the GO-SF bionanofilms.The difference in the stress and strain distribution inside the GO-SF bionanofilms before and after annealing was analyzed.In addition,the load-displacement curves that were obtained by the developed FEA model conformed well with the results from the nanoindentation tests.In summary,this study presents the mechanism of improving the indentation modulus and hardness of the GO-SF bionanofilms through the water vapor annealing process,which is established with the FEA simulation models. | Hyeonho CHO Joonho LEE Hyundo HWANG Woonbong HWANG Jin-Gyun KIM Sunghan KIM | 2022 | Friction2022，10，2： | 0 |
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