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과리튬 층상 산화물 양극재의 성능 향상을 위한 도펀트의 전산 스크리닝
강승표(Seungpyo Kang),박현규(Hyungyu Park),정진영(Jinyoung Jeong),박태현(Taehyun Park),나성민(Sungmin Na),안현진(Hyunjin An),박광진(Kwangjin Park),민경민(Kyoungmin Min) 대한기계학회 2023 대한기계학회 춘추학술대회 Vol.2023 No.11
Over-lithiated layered oxide (OLO) enables enhanced specific capacity with additional Li-ion in the transition metal. Nevertheless, extreme delithiation causes reversible oxygen redox and structural modifications, resulting in undesirable potential and capacity losses during charge/discharge cycles. To solve this problem, a doping strategy was used. Computational screening based on first-principles calculations was performed to select the ideal dopant. Li₁ ₄Ni₀ <SUB>25</SUB>Co₀ ₁Mn0 <SUB>65</SUB>O₂ ₄ is used to confirm the improvement in structural and electrochemical performance of the considered dopants. During screening, the criteria of formation energy and volume change are considered. Among the initial 29 candidates, 21 dopants can improve the electrochemical performance of the pristine structure. The top five candidates are Sr, Ba, Ca, Rh, and Ta. This research can accelerate development of stable and high-performance cathode for lithium-ion batteries.
생성모델을 적용한 나트륨이온 배터리용 슈퍼 이온전도성 고체 전해질 설계
강승표(Seungpyo Kang),민경민(Kyoungmin Min) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
Na-Ion Batteries(NIBs) is an alternative to Li-Ion Batteries(LIBs). Solid-state electrolytes solve critical safety issues of liquid electrolyte. In this study, we developed a platform involving generative model, high-throughput screening process and a machine learning surrogate model for identifying superionic Na-SSEs among Na-containing materials. Through the screening process, materials which showed potentially superior performance are selected, and their ionic conductivity were predicted. The surrogated model was constructed by ensembling two models with the best performance. 100 predictions were made for each model. In all 100 times, materials predicted to be superionic were inter-aggregated, and no material was recommended. Detail structural Descriptor will be considered. After this consideration, we believe this platform will accelerate the search for Na-SSEs with high ionic conductivity at a minimum cost.
생성모델을 적용한 나트륨이온 배터리용 슈퍼 이온전도성 고체 전해질 설계
강승표(Seungpyo Kang),민경민(Kyoungmin Min) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
Na-Ion Batteries(NIBs) is an alternative to Li-Ion Batteries(LIBs). Solid-state electrolytes solve critical safety issues of liquid electrolyte. In this study, we developed a platform involving generative model, high-throughput screening process and a machine learning surrogate model for identifying superionic Na-SSEs among Na-containing materials. Through the screening process, materials which showed potentially superior performance are selected, and their ionic conductivity were predicted. The surrogated model was constructed by ensembling two models with the best performance. 100 predictions were made for each model. In all 100 times, materials predicted to be superionic were inter-aggregated, and no material was recommended. Detail structural Descriptor will be considered. After this consideration, we believe this platform will accelerate the search for Na-SSEs with high ionic conductivity at a minimum cost.
기계 학습을 통한 새로운 가넷형 고체 전해질 후보 발견 가속화
선지원(Jiwon Sun),강승표(Seungpyo Kang),김준철(Joonchul Kim),민경민(Kyoungmin Min) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
All-Solid-State-Batteries brought extensive attraction because of their higher energy density and stability than conventional Lithium-Ion-Batteries. For the development of promising ASSBs, Solid-State Electrolytes is an essential component for achieving structural integrity. Thus, in this study, a machine learning-based surrogate model was developed to search for ideal garnet-type SSE candidates. Well-known Li7La3Zr2O12 structure is used as a base material, and 73 chemical elements substitute La- and Zr-site, leading to 5,329 potential structures. First, the elastic database was adopted from previous research. Then the machine learning-based surrogate model was applied to predict the elastic properties of potential SSE materials, followed by the first-principles calculations for validation. Furthermore, the active learning process demonstrates that it can reduce prediction uncertainty. Finally, the ionic conductivity of mechanically superior materials is predicted for suggesting optimal SSE candidates. We believe that the current model and constructed database become a cornerstone for developing next-generation SSE materials.
기계 학습을 적용한 나트륨 초이온 전고체(NASICON) 전해질 탐색 방법
김주오(Juo Kim),강승표(SeungPyo Kang),민경민(Kyoungmin Min) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
Sodium-ion battery is considered promising alternatives as similar types of Lithium-ion battery because of their low manufacturing cost, wide abundance, and similar chemical/electrochemical properties. Among them, numerous studies are being conducted on solid state electrolytes(SSEs) that can solve the flammability problem of existing liquid state electrolytes(LSEs). Since the SSE tends to have lower ionic conductivity than the LSEs, research into finding a SSEs having ionic conductivity close to LSEs has been actively conducted. This research suggests a method to find a superionic materials among sodium (Na) superionic conductor (NASICON), one of the sodium-ion SSE, through machine learning. Various machine learning classification models were compared for 3,585 NASICON candidate materials, and the highest model showed an average accuracy of 0.84. Structural information was generated for the selected materials using Ewald summation, and the performance of the model is being verified through DFT calculation based on this structural information. Through this study, it is expected to accelerate the speed of NASICON material search.
기계 학습을 적용한 나트륨 초이온 전고체(NASICON) 전해질 탐색 방법
김주오(Juo Kim),강승표(SeungPyo Kang),민경민(Kyoungmin Min) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
Sodium-ion battery is considered promising alternatives as similar types of Lithium-ion battery because of their low manufacturing cost, wide abundance, and similar chemical/electrochemical properties. Among them, numerous studies are being conducted on solid state electrolytes(SSEs) that can solve the flammability problem of existing liquid state electrolytes(LSEs). Since the SSE tends to have lower ionic conductivity than the LSEs, research into finding a SSEs having ionic conductivity close to LSEs has been actively conducted. This research suggests a method to find a superionic materials among sodium (Na) superionic conductor (NASICON), one of the sodium-ion SSE, through machine learning. Various machine learning classification models were compared for 3,585 NASICON candidate materials, and the highest model showed an average accuracy of 0.84. Structural information was generated for the selected materials using Ewald summation, and the performance of the model is being verified through DFT calculation based on this structural information. Through this study, it is expected to accelerate the speed of NASICON material search.
김민선(Minseon Kim),강승표(Seungpyo Kang),민경민(Kyoungmin Min) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11
Demand for developing sustainable battery technology is rapidly growing. Lithium- and sodium-ion batteries are secondary batteries that have advantages of high-power, high-energy density, and long life and are critical elements of energy storage devices. However, due to the high-price and environmental problems of Co and Ni, which are core elements of battery cathode materials, it is necessary to develop next-generation cathode materials that can replace them without scarifying the performance. However, testing and calculating thousands of new cathode candidates satisfying the ideal performance criteria requires extreme resources. Therefore, in this study, we create a model to predict the capacity, operating voltage, and structural stability of cathode materials using machine learning and develop a platform to screen next-generation cathode materials.
기계 학습을 통한 새로운 가넷형 고체 전해질 후보 발견 가속화
선지원(Jiwon Sun),강승표(Seungpyo Kang),김준철(Joonchul Kim),민경민(Kyoungmin Min) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
All-Solid-State-Batteries brought extensive attraction because of their higher energy density and stability than conventional Lithium-Ion-Batteries. For the development of promising ASSBs, Solid-State Electrolytes is an essential component for achieving structural integrity. Thus, in this study, a machine learning-based surrogate model was developed to search for ideal garnet-type SSE candidates. Well-known Li7La3Zr2O12 structure is used as a base material, and 73 chemical elements substitute La- and Zr-site, leading to 5,329 potential structures. First, the elastic database was adopted from previous research. Then the machine learning-based surrogate model was applied to predict the elastic properties of potential SSE materials, followed by the first-principles calculations for validation. Furthermore, the active learning process demonstrates that it can reduce prediction uncertainty. Finally, the ionic conductivity of mechanically superior materials is predicted for suggesting optimal SSE candidates. We believe that the current model and constructed database become a cornerstone for developing next-generation SSE materials.
[가솔린엔진부문] 가솔린 엔진의 비정상상태에 대한 Map 구성과 공기 및 연료 모델 개선
심연섭(Yonsob Shim),강태성(Taeseong Kang),강승표(Seungpyo Kang),고상근(Sang Ken Kauh) 한국자동차공학회 2000 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
For gasoline engines, a three-way catalyst converter is being used for cleaning up the exhaust gas contamination. Precise air/fuel ratio control is necessary to maximize the catalytic conversion efficiency. In transient condition, feedforward air/fuel ratio control that estimates air mass inducted into the cylinder is performed.<br/> In this study, a fuel injection map that makes accurate air/fuel ratio control possible was constructed for the very same transient condition. In the same condition above, intake air model and fuel model were refined so that fuel injection values based on air mass flow through throttle valve and intake manifold pressure are equal to fuel injection values of the map.<br/>