Nagoya University · 재료과학
이 연구실은 전자기적 및 열역학적 성질을 갖는 다결정계의 결정립 경계(GB)에서의 원자 구조와 에너티크스를 정밀하게 예측하기 위해 인공지능 기반의 상호작용 잠재에너지 모델(ANN 잠재에너지)을 핵심 도구로 활용합니다. 주로 실리콘, 아연산화아연, 알루미늄, 산화지르코니아, 산화마그네슘 등 다양한 반도체 및 산화물 재료에서의 결함, 경계 에너지, 열역학적 안정성 및 진동 엔트로피 기여를 다룹니다. 특히 DFT 계산과 결합한 몬테카를로 시뮬레이션, 분자역학, 격자 동역학 분석을 통해 원자 수준에서의 경계 상호작용 기작을 규명하고 있습니다. 이는 고온 안정성, 재료의 피로 거동, 이온 도핑 효과 등 응용 분야로 이어지는 기초 연구입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Artificial neural-network (ANN) interatomic potentials for simulating atomic structures and energetics of grain boundaries (GBs) in silicon were constructed and integrated into structural optimization and molecular dynamics (MD) algorithms. A training dataset including various atomic environments of symmetric tilt GBs was generated by performing density-functional-theory (DFT) calculations. The ANN potential after training was found to be capable of approximating the potential-energy surface at
The energetically favorable spatial configuration of M(3+) ions and oxide-ion vacancies near a symmetrical grain boundary (GB) in cubic zirconia is determined for various trivalent species M(3+) (M = Al, Sc, Y, Gd, La), and the driving force for grain boundary segregation (GBS) quantitatively examined using atomistic Monte Carlo simulations in conjunction with static lattice calculations. For a high concentration of ∼10 mol %, it is found that point defects near a GB plane preferentially occupy
To accurately predict grain boundary (GB) atomic structures and their energetics in CdTe, the present study constructs an artificial-neural-network (ANN) interatomic potential. To cover a wide range of atomic environments, large amounts of density functional theory (DFT) data are used as a training dataset including point defects, surfaces and GBs. Structural relaxation combined with the trained ANN potential is applied to symmetric tilt and twist GBs, many of which are not included in the train
An artificial neural network (ANN) potential for Al, trained with density-functional-theory (DFT) data, is constructed to accurately predict lattice vibrational properties and thermodynamics of grain boundaries (GBs) in Al. The ANN potential is demonstrated to accurately predict not only atomic structures and energetics of the GBs at 0 K but also partial phonon densities of states and vibrational entropies, even for GBs absent in the training data sets. In addition, their total potential energie
First-principles lattice dynamics calculations were performed to reveal an atomic-level origin of excess vibrational entropies at grain boundaries (GBs) in MgO. Fourteen symmetric tilt GBs with various structural units were systematically examined. The excess vibrational entropies were found to vary depending on the individual GBs, and as a result, the relative thermodynamic stability of the GBs studied changed with temperature. The excess GB volumes were less correlated with the excess vibratio
The site-selective occupation of point defects, Y3+ ions (Y′Zr) and O2− vacancies (V\ddot{O}), and their associations at a symmetric tilt grain boundary (GB) are studied to understand their competitive contribution to energetically favorable atomic arrangements by using atomistic simulations. It is found that at the GB there are the favorable sites for segregation of an isolated Y′Zr and V\ddot{O}. This indicates that the driving force for the site-selective segregation is present. Moreover, our
First-principles lattice dynamics is applied to symmetric tilt grain boundaries (GBs) in Al, Si and MgO, with the goal of revealing critical factors in determining excess vibrational entropies at the atomic level. Excess vibrational entropies at GBs are found to vary depending on the substances. Al GBs tend to show larger excess entropies and hence larger temperature dependence of the GB free energies than those in Si and MgO. Most of the Si GBs show small excess entropies. For Al and MgO, atom-
