Dislocations in ceramics are increasingly recognized for their promising potential in applications such as toughening intrinsically brittle ceramics and tailoring functional properties.However,the atomistic simulation...Dislocations in ceramics are increasingly recognized for their promising potential in applications such as toughening intrinsically brittle ceramics and tailoring functional properties.However,the atomistic simulation of dislocation plasticity in ceramics remains challenging due to the complex interatomic interactions characteristic of ceramics,which include a mix of ionic and covalent bonds,and highly distorted and extensive dislocation core structures within complex crystal structures.These complexities exceed the capabilities of empirical interatomic potentials.Therefore,constructing neural network potentials(NNPs)emerges as the optimal solution.Yet,creating a training dataset that includes dislocation structures proves difficult due to the complexity of their core configurations in ceramics and the computational demands of density functional theory for large atomic models containing dislocation cores.In this work,we propose a training dataset from properties that are easier to compute via high-throughput calculation.展开更多
基金supported by the JSPS Postdoctoral Fellowships for Research in Japan(Standard)the Grant-in-Aid for JSPS Research Fellow(GrantNo.22F22056)+4 种基金the JSPSKAKENHI(GrantNo.JP22KF-241)were used computational resources of supercomputer Fugaku provided by the RIKEN Center for Computational Science(Project IDs:hp230205 and hp230212)the large-scale computer systems at the Cybermedia Center,Osaka University,and the Large-scale parallel computing server at the Center for Computational Materials Science,Institute for Materials Research,Tohoku University.S.O.acknowledges the support by the Ministry of Education,Culture,Sport,Science and Technology of Japan(Grant Nos.JPMXP1122684766,JPMXP1020230325,and JPMXP1020230327)the support by JSPS KAKENHI(Grant Nos.JP23H00161 and JP23K20037)the support by the Japan Society for the Promotion of Science(JSPS)KAKENHI JP24H00285,JP24H00032,JP24K17169,and JP22K14143.
文摘Dislocations in ceramics are increasingly recognized for their promising potential in applications such as toughening intrinsically brittle ceramics and tailoring functional properties.However,the atomistic simulation of dislocation plasticity in ceramics remains challenging due to the complex interatomic interactions characteristic of ceramics,which include a mix of ionic and covalent bonds,and highly distorted and extensive dislocation core structures within complex crystal structures.These complexities exceed the capabilities of empirical interatomic potentials.Therefore,constructing neural network potentials(NNPs)emerges as the optimal solution.Yet,creating a training dataset that includes dislocation structures proves difficult due to the complexity of their core configurations in ceramics and the computational demands of density functional theory for large atomic models containing dislocation cores.In this work,we propose a training dataset from properties that are easier to compute via high-throughput calculation.
