2024
DOI: 10.3390/met14040415
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Molecular Dynamics Study of Temperature Dependence of Grain Boundaries (100) in Pure Aluminum with Application of Machine Learning

Evgenii V. Fomin

Abstract: As is known, grain boundary (GB) energy determines the mobility of GBs and their population in metals. In this work, we study the energy of GBs in the (100) crystallographic plane and in the temperature range from 100 to 700 K. The study is carried out using both the molecular dynamic (MD) method and machine learning approach to approximate the MD data in order to obtain functional dependence in the form of a feed-forward neural network (FCNN). We consider the tilt and twist grain boundaries in the range of mi… Show more

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