arrow
返回

Modular development of deep potential for complex solid solutions

delete2023-04-12
delete19
delete
OA
AI
J
Jing Wu
J
Jiyuan Yang
L
Liyang Ma
L
Linfeng Zhang
S
Shi Liu *
DOI:10.1103/PhysRevB.107.144102delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
The multicomponent oxide solid solution is a versatile platform to tune the delicate balance between competing spin, charge, orbital, and lattice degrees of freedom for materials design and discovery. The development of compositionally complex oxides with superior functional properties has been largely empirical and serendipitous, in part due to the exceedingly complex chemistry and structure of solid solutions that span a range of length scales. The usage of classical molecular dynamics (MD), a powerful statistical method, in computer-aided materials design has not yet reached the same level of sophistication as that in computer-aided drug design because of the limited availability and accuracy of classical force fields for solids. Here, we introduce the strategy of modular development of deep potential (ModDP) that enables a systematic development and improvement of deep-neural-network-based model potential, termed as deep potential, for complex solid solutions with minimum human intervention. The converged training database associated with an end-member material is treated as an independent module and is reused to train the deep potential of solid solutions via a concurrent learning procedure. We apply ModDP to obtain classical force fields of two technologically important solid solutions, PbxSr1-xTiO3 and HfxZr1-xO2. For both materials' systems, a single model potential is capable of predicting various properties of solid solutions including temperature-driven and composition-driven phase transitions over a wide range of compositions. In particular, the deep potential of PbxSr1-xTiO3 reproduces a few known topological textures such as polar vortex lattice and electric dipole waves in PbTiO3/SrTiO3 superlattices, paving the way for MD investigations on the dynamics of topological structures in response to external stimuli. MD simulations of HfxZr1-xO2 reveal a substantial impact of composition variation on both the phase transition temperature and the nature of the high-temperature nonpolar phase.
Keyword:
MOLECULAR-DYNAMICS SIMULATION
TOTAL-ENERGY CALCULATIONS
PHASE-TRANSITION
FORCE-FIELD
PEROVSKITE
FERROELECTRICITY
PBTIO3
LATTICE
SRTIO3
TEMPERATURE

期刊

Physical Review B 封面图
Physical Review B
IF:
3.7
论文数:
15.4W
被引数:
41.0W

机构

W
westlake university
学者数:
5.3K
论文数: 3.7K
被引数: 8
引用论文

引用论文

The role of reduced graphene oxide on chemical, mechanical and barrier properties of natural rubber composites
err2014-10-01
err0
PREAI
errNing Yan; Giovanna Buonocore; Marino Lavorgna; Saulius Kaciulis; Santosh Kiran Balijepalli; Yanhu Zhan; Hesheng Xia; Luigi Ambrosio
err分享
err收藏
Surface and grain boundary energy as the key enabler of ferroelectricity in nanoscale hafnia-zirconia: a comparison of model and experiment表面和晶界能是纳米级氧化铪-氧化锆中铁电的关键推动力: 模型和实验的比较
err2017-01-01
err305
PREAI
errPark, Min Hyuk; Lee, Young Hwan; Kim, Han Joon; Schenk, Tony; Lee, Woongkyu; Do Kim, Keum; Fengler, Franz P. G.; Mikolajick, Thomas; Schroeder, Uwe; Hwang, Cheol Seong
err分享
err收藏
The Heavy Mineral Map of Australia Project – Data Release 2: The Barkly-Isa-Georgetown Region
err
IF0
err2022-01-01
err0
PREAI
errP. de Caritat; E. Bastrakov; A.T. Walker; B.I.A. McInnes
err分享
err收藏
Lattice dynamics of BaTiO3, PbTiO3, and PbZrO3:: A comparative first-principles study
err1999-07-01
err424
errOAAI
errGhosez, P; Cockayne, E; Waghmare, UV; Rabe, KM
err分享
err收藏
err分享
err收藏
Atomic mapping of periodic dipole waves in ferroelectric oxide
err2021-07-09
err44
errOAAI
errGong, Feng-Hui; Tang, Yun-Long; Zhu, Yin-Lian; Zhang, Heng; Wang, Yu-Jia; Chen, Yu-Ting; Feng, Yan-Peng; Zou, Min-Jie; Wu, Bo; Geng, Wan-Rong; Cao, Yi; Ma, Xiu-Liang
err分享
err收藏
学者 查看更多内容