Machine learning modeling of high entropy alloy: the role of short-range order

Abstract

The development of machine learning sheds new light on the traditionally complicated problem of thermodynamics in multicomponent alloys. Successful application of such a method, however, strongly depends on the quality of the data and model. Here we propose a scheme to improve the representativeness of the data by utilizing the short-range order (SRO) parameters to survey the configuration space. Using the improved data, a pair interaction model is trained for the NbMoTaW high entropy alloy using linear regression. Benefiting from the physics incorporated into the model, the learned effective Hamiltonian demonstrates excellent predictability over the whole configuration space. By including pair interactions within the 6th nearest-neighbor shell, this model achieves an $R^2$ testing score of 0.997 and root mean square error of 0.43 meV. We further perform a detailed analysis on the effects of training data, testing data, and model parameters. The results reveal the vital importance of representative data and physical model. On the other hand, we also examined the performance neural networks, which is found to demonstrate a strong tendency to overfit the data.

arXiv comment15 pages, 9 figures

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Cited by in corpus (4)

• Robust data-driven approach for predicting the configurational energy of high entropy alloys
• Chemical complexity in high entropy alloys: A pair-interaction perspective
• Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: a data-driven approach
• Temperature dependent phase stability of Mo-Nb-Ta-W refractory high-entropy alloys