Development of a Phase Field Tool Coupling With Thermodynamic Data for Microstructure Evolution Simulation of Alloys in Nuclear Reactors

Guo, Yi; Li, Yifan; Liu, Zhen; Shi, Diwei; Song, Jiexi; Zhang, Biao; Bu, Moran; Du, Shiyu

doi:10.3389/fmats.2021.627864

Cited by 2 publications

(1 citation statement)

References 57 publications

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“…However, in many materials, data are difficult to obtain and do not cover a wide range of compositions, such as FeCrAl alloys. In fact, the FeCrAl alloy is one of the most popular candidates for nuclear fuel cladding materials. − At the same time, multiscale researches rely heavily on high-precision MLFFs, but the FNN architecture is limited in terms of accuracy and efficiency gains from descriptors with scarce data volume. , …”

Section: Introductionmentioning

confidence: 99%

AisNet: A Universal Interatomic Potential Neural Network with Encoded Local Environment Features

Guo

Liu

et al. 2023

J. Chem. Inf. Model.

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This paper proposes a new interatomic potential energy neural network, AisNet, which can efficiently predict atomic energies and forces covering different molecular and crystalline materials by encoding universal local environment features, such as elements and atomic positions. Inspired by the framework of SchNet, AisNet consists of an encoding module combining autoencoder with embedding, the triplet loss function and an atomic central symmetry function (ACSF), an interaction module with a periodic boundary condition (PBC), and a prediction module. In molecules, the prediction accuracy of AisNet is comparabel with SchNet on the MD17 dataset, mainly attributed to the effective capture of chemical functional groups through the interaction module. In selected metal and ceramic material datasets, the introduction of ACSF improves the overall accuracy of AisNet by an average of 16.8% for energy and 28.6% for force. Furthermore, a close relationship is found between the feature ratio (i.e., ACSF and embedding) and the force prediction errors, exhibiting similar spoon-shaped curves in the datasets of Cu and HfO2. AisNet produces highly accurate predictions in single-commponent alloys with little data, suggesting the encoding process reduces dependence on the number and richness of datasets. Especially for force prediction, AisNet exceeds SchNet by 19.8% for Al and even 81.2% higher than DeepMD on a ternary FeCrAl alloy. Capable of processing multivariate features, our model is likely to be applied to a wider range of material systems by incorporating more atomic descriptions.

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