| Summary: | 碩士 === 國立臺灣大學 === 應用力學研究所 === 107 === High Entropy Alloys (HEA) is a novel metallic material that has drawn increasing attentions from both academia and industries in recent years. In contrast to conventional alloys comprising of at most one or two principal elements, HEA system consists of more than four constituent elements with concentrations ranging between 5% and 35%. The entropic mixing effect arising from high configuration entropy ensures the HEAs to remain the simple solid solution phase with FCC, BCC or HCP crystal structures without phase segregations. The lattice distortion effect from atomic size differences makes HEA has unique thermal, electric, and mechanical properties such as low thermal/electric conductivities. Lattice distortion also slows down defect diffusion, brings obstacles to dislocation glides and prevents oxidation at elevated temperatures. Finally, the cocktail effect, namely, mixing atoms of distinct properties together, allows tuning material properties of HEAs with almost infinite degrees of freedom. These aforementioned features make HEAs emerge as the rising star as the future structural and electric materials. In this thesis, we attempted to investigate the material properties of Fe-Co-Ni-Cr HEA by using machine-learning-enabled atomistic simulations.
In atomistic simulations, the first principle calculations provide the most accurate system energies and atomic forces. However, the first principle calculations are computationally expensive, thereby preventing performing exhaustive sampling of configurations of HEAs. Classical molecular dynamics simulations (MD) allow efficient exploration of atomistic configurational space; however, the accuracy of MD calculations critically relies on the empirical force fields (or, potentials). In this thesis, by harnessing the power of machine learning, we employed the neural network potential model (NN) to predict the potential energy of HEA. The random arrangement of atoms makes training very challenging.
In this thesis, by computing the material properties of CoCrFeNi HEA with the trained NN potential, we demonstrated that the NN potential energy predictor can successfully reproduce results from first principle calculations with errors around 1-5%. Nonetheless, the accuracy of NN energy predictors critically rely on the selection training sets. The NN potential model offers a hundreds of thousand times computational speedup than first principle calculations while retaining the computational accuracies, thereby allowing atomistic Monte Carlo method and molecular dynamics simulations to explore the configurations and chemical short-range orders of high entropy alloys.
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