The computing technique for computations of phase fields,as a fundamental and critical technology in such fields as designing of steel materials,faces the main challenge of balancing calculation accuracy and efficiency.With the rapid development of the computation field for materials science,machine learning has opened up a new way for improving the efficiency and accuracy of phase field computations.By comprehensively reviewing the applications of machine learning in accelerating phase field calculations,different strategies and implementation methods for solving phase fields and similar partial differential equations by using machine learning technology were summarized,and the calculation results by these methods were evaluated.By comparing and analyzing the advantages,limitations and applicable scenarios of various machine learning methods in accelerating phase field computations,the current challenges and future development directions were discussed,which provided directive guidance for carrying out the studies of accelerating phase field calculations by machine learning.
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