Irradiated mechanical properties predicted by a machine learning method with the Fourier-transform-based feature extension

ABSTRACTAfter irradiation, the variation of yield strength in metallic materials is multivariate nonlinear. High-dimensional nonlinear relationships between the irradiated yield strength and its influencing factors, including material properties, doses, irradiation temperatures, and crystal structures, etc. are difficult to explicitly characterize in the absence of a comprehensive database. In this study, we developed a machine learning method with the Fourier-transform-based feature extension, successfully constructing the prediction model of irradiated yield strength by a relatively small and sparse database of irradiated material properties. The analysis suggests that the proposed feature extension method improves the training performances of machine learning with small dataset. And the present model is accurate and feasible for predicting the irradiated yielding behaviors. Furthermore, we attempt the inverse machine learning model to determine material properties and irradiation conditions according to the desired yield strength. Since the parameter combinations commensurate with a fixed strength are diverse, the optimal model is helpful in reversely calculating and optimizing material performances. The data-driven machine learning method, which can detect the implicit correlations among numerous data, exhibits great prospects in investigating irradiated mechanical properties and exploring multiscale links in the nuclear material field. This work holds the promise for optimizing the design of in-pile structural components and can be further extended to other machine learning problems with the small dataset.KEYWORDS: Yield strengthmachine learningirradiationDimensional extension method of feature vectorSupported vector machine for regressionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. AcknowledgmentsThis work was supported by the Natural Science Foundation of China (12172270), the Fund of Science and Technology on Reactor Fuel and Materials Laboratory (6142A06190111), the Project of Nuclear Power Institute of China (No. K902023-04-FW-HT-20220003), the Youth Science and Technology Innovation Team Project of China National Nuclear Corporation (JT211), the Qin Chuangyuan “Scientists+Engineers” Team Construction Project in Shaanxi Province (2022KXJ-085), and the Innovative scientific Program of CNNC. The computation has made use of the High Performance Computing (HPC) platform of Xi’ an Jiaotong University.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work was supported by the National Natural Science Foundation of China [12172270]; the Fund of Science and Technology on Reactor Fuel and Materials Laboratory [6142A06190111].