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浏览/检索结果: 共5条，第1-5条 帮助 限定条件 发表日期：2022     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 An emb e dde d-atom method potential for studying the properties of Fe-Pb solid-liquid interface 期刊论文 JOURNAL OF NUCLEAR MATERIALS, 2022, 卷号: 572 作者:  Lei, Yawei;  Zhang, Jingdan;  Zhang, Yange;  Li, Xiangyan;  Xu, Yichun 收藏  |  浏览/下载：19/0  |  提交时间：2022/12/23 Fe-Pb system  Force -matching method  EAM potential  Corrosion  Liquid properties   Radiation damage accumulation mechanisms at iron grain boundaries revealed by coupled atomic and coarse-grained simulations via the parameter-passing and structural feedback 期刊论文 JOURNAL OF NUCLEAR MATERIALS, 2022, 卷号: 572 作者:  Li, Xiangyan;  Li, Xiaolin;  Zhang, Yange;  Xu, Yichun;  Wu, Xuebang 收藏  |  浏览/下载：20/0  |  提交时间：2022/12/23 Grain boundary  Multiscale simulation  Structural relaxation  Defects clusters   Effects of oxygen concentration and irradiation defects on the oxidation corrosion of body-centered-cubic iron surfaces: A first-principles study 期刊论文 CHINESE PHYSICS B, 2022, 卷号: 31 作者:  Ye, Zhiqiang;  Lei, Yawei;  Zhang, Jingdan;  Zhang, Yange;  Li, Xiangyan 收藏  |  浏览/下载：15/0  |  提交时间：2022/12/23 oxidation corrosion  oxygen concentration  irradiation defects  bcc-Fe surfaces  first-principles study   Simulation and experimental studies of the dissolution corrosion of 4H-SiC in liquid Pb/Bi 期刊论文 APPLIED SURFACE SCIENCE, 2022, 卷号: 585 作者:  Lei, Yawei;  Zhang, Yange;  Li, Xiangyan;  Xu, Yichun;  Wu, Xuebang 收藏  |  浏览/下载：24/0  |  提交时间：2022/05/16 Silicon carbide  Liquid Pb  Bi  First-principles calculations  Dissolution corrosion  Corrosion resistance   Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method 期刊论文 JOURNAL OF NUCLEAR MATERIALS, 2022, 卷号: 559 作者:  Wang, Yuxuan;  Li, Xiaolin;  Li, Xiangyan;  Zhang, Yuxiang;  Zhang, Yange 收藏  |  浏览/下载：14/0  |  提交时间：2022/12/23 Vacancy formation energy  Machine learning  Tungsten  Symmetry tilt grain boundary  Support vector machine  Cross validation