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| 1 | Computational discovery of p-type transparent oxide semiconductors using hydrogen descriptor显示文摘The ultimate transparent electronic devices require complementary and symmetrical pairs of n-type and p-type transparent semiconductors.While several n-type transparent oxide semiconductors like InGaZnO and ZnO are available and being used in consumer electronics,there are practically no p-type oxides that are comparable to the n-type counterpart in spite of tremendous efforts to discover them.Recently,high-throughput screening with the density functional theory calculations attempted to identify candidate p-type transparent oxides,but none of suggested materials was verified experimentally,implying need for a better theoretical predictor.Here,we propose a highly reliable and computationally efficient descriptor for p-type dopability—the hydrogen impurity energy.We show that the hydrogen descriptor can distinguish well-known p-type and n-type oxides.Using the hydrogen descriptor,we screen most binary oxides and a selected pool of ternary compounds that covers Sn^(2+)-bearing and Cu^(1+)-bearing oxides as well as oxychalcogenides.As a result,we suggest La_(2)O_(2)Te and CuLiO as promising p-type oxides. | Kanghoon Yim Yong Youn Miso Lee Dongsun Yoo Joohee Lee Sung Haeng Cho Seungwu Han | 2018 | npj Computational Materials2018，，1： | 2 |
| 2 | Effects of Oxygen Adsorption on Carbon Nanotube Field Emitters显示文摘 | Noejung Park Seungwu Han Jisoon Ihm | | Physical Review B0，04，： | 1 |
| 3 | Effects of Oxygen Adsorption on Carbon Nanotube Field Emitters显示文摘 | Noejung Park Seungwu Han Jisoon Ihm | 2001 | Phys Rev B2001，64，： | 1 |
| 4 | Effects of Oxygen Adsorption on Carbon Nanotube Field Emitters显示文摘 | Park Noejung Han Seungwu Ihm Jisoon | 2001 | Phys Rev B2001，64，： | 1 |
| 5 | Ab initio study on the field emission from linear carbon chains and carbon nanotubes显示文摘 | Seungwu Han | 2001 | Journal of the Korean Physical Society2001，39，3： | 1 |
| 6 | Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials显示文摘The discovery of multicomponent inorganic compounds can provide direct solutions to scientific and engineering challenges,yet the vast uncharted material space dwarfs synthesis throughput.While the crystal structure prediction(CSP)may mitigate this frustration,the exponential complexity of CSP and expensive density functional theory(DFT)calculations prohibit material exploration at scale.Herein,we introduce SPINNER,a structure-prediction framework based on random and evolutionary searches.Harnessing speed and accuracy of neural network potentials(NNPs),the program navigates configurational spaces 10^(2)–10^(3) times faster than DFT-based methods.Furthermore,SPINNER incorporates algorithms tuned for NNPs,achieving performances exceeding conventional algorithms.In blind tests on 60 ternary compositions,SPINNER identifies experimental(or theoretically more stable)phases for~80%of materials.When benchmarked against data-mining or DFT-based evolutionary predictions,SPINNER identifies more stable phases in many cases.By developing a reliable and fast structure-prediction framework,this work paves the way to large-scale,open exploration of undiscovered inorganic crystals. | Sungwoo Kang Wonseok Jeong Changho Hong Seungwoo Hwang Youngchae Yoon Seungwu Han | 2022 | npj Computational Materials2022，，1： | 0 |
| 7 | Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials显示文摘The universal mathematical form of machine-learning potentials(MLPs)shifts the core of development of interatomic potentials to collecting proper training data.Ideally,the training set should encompass diverse local atomic environments but conventional approaches are prone to sampling similar configurations repeatedly,mainly due to the Boltzmann statistics.As such,practitioners handpick a large pool of distinct configurations manually,stretching the development period significantly.To overcome this hurdle,methods are being proposed that automatically generate training data.Herein,we suggest a sampling method optimized for gathering diverse yet relevant configurations semi-automatically.This is achieved by applying the metadynamics with the descriptor for the local atomic environment as a collective variable.As a result,the simulation is automatically steered toward unvisited local environment space such that each atom experiences diverse chemical environments without redundancy.We apply the proposed metadynamics sampling to H:Pt(111),GeTe,and Si systems.Throughout these examples,a small number of metadynamics trajectories can provide reference structures necessary for training high-fidelity MLPs.By proposing a semiautomatic sampling method tuned for MLPs,the present work paves the way to wider applications of MLPs to many challenging applications. | Dongsun Yoo Jisu Jung Wonseok Jeong Seungwu Han | 2021 | npj Computational Materials2021，，1： | 0 |
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