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Phase transitions of zirconia: Machine-learned force fields beyond density functional theory
Liu, Peitao1,2,3,4; Verdi, Carla2,3; Karsai, Ferenc1; Kresse, Georg1,2,3
Corresponding AuthorLiu, Peitao(peitao.liu@univie.ac.at)
2022-02-16
Source PublicationPHYSICAL REVIEW B
ISSN2469-9950
Volume105Issue:6Pages:6
AbstractMachine-learned force fields (MLFFs) are increasingly used to accelerate first-principles simulations of many materials properties. However, MLFFs are generally trained from density functional theory (DFT) data and thus suffer from the same limitations as DFT. To achieve more predictive accuracy, MLFFs based on higher levels of theory are required, but the training becomes exceptionally arduous. Here, we present an approach to generate MLFFs with beyond DFT accuracy which combines an efficient on-the-fly active learning method and Delta-machine learning. Using this approach, we generate an MLFF for zirconia based on the random phase approximation (RPA). Specifically, an MLFF trained on the fly during DFT-based molecular dynamics simulations is corrected by another MLFF that is trained on the differences between RPA and DFT calculated energies, forces, and stress tensors. We show that owing to the relatively smooth nature of these differences, the expensive RPA calculations can be performed only on a small number of representative structures of small unit cells selected by rank compression of the kernel matrix. This dramatically reduces the computational cost and allows one to generate an MLFF fully capable of reproducing high-level quantum-mechanical calculations beyond DFT. We carefully validate our approach and demonstrate its success in studying the phase transitions of zirconia. These results open the way to many-body calculations of finite-temperature properties of materials.
Funding OrganizationAdvanced Materials Simulation Engineering Tool (AMSET) project - US Naval Nuclear Laboratory (NNL) ; Austrian Science Fund (FWF) within the SFB TACO
DOI10.1103/PhysRevB.105.L060102
Indexed BySCI
Language英语
Funding ProjectAdvanced Materials Simulation Engineering Tool (AMSET) project - US Naval Nuclear Laboratory (NNL) ; Austrian Science Fund (FWF) within the SFB TACO[F 81-N]
WOS Research AreaMaterials Science ; Physics
WOS SubjectMaterials Science, Multidisciplinary ; Physics, Applied ; Physics, Condensed Matter
WOS IDWOS:000761166700002
PublisherAMER PHYSICAL SOC
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Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/173229
Collection中国科学院金属研究所
Corresponding AuthorLiu, Peitao
Affiliation1.VASP Software GmbH, Sensengasse 8, A-1090 Vienna, Austria
2.Univ Vienna, Fac Phys, Kolingasse 14-16, A-1090 Vienna, Austria
3.Univ Vienna, Ctr Computat Mat Sci, Kolingasse 14-16, A-1090 Vienna, Austria
4.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang, Peoples R China
Recommended Citation
GB/T 7714
Liu, Peitao,Verdi, Carla,Karsai, Ferenc,et al. Phase transitions of zirconia: Machine-learned force fields beyond density functional theory[J]. PHYSICAL REVIEW B,2022,105(6):6.
APA Liu, Peitao,Verdi, Carla,Karsai, Ferenc,&Kresse, Georg.(2022).Phase transitions of zirconia: Machine-learned force fields beyond density functional theory.PHYSICAL REVIEW B,105(6),6.
MLA Liu, Peitao,et al."Phase transitions of zirconia: Machine-learned force fields beyond density functional theory".PHYSICAL REVIEW B 105.6(2022):6.
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