Using Physical Parameters for Phase Prediction of Multi-Component Alloys by the Help of TensorFlow Machine Learning with Limited Data

In recent years developing new material and compounds have become more important because of the community’s needs. Material scientist and physicist great effort make significant changes in daily life. But nowadays it is important to make these changes in a short time. In this point of view, artificial intelligence and machine learning gives the scientist a great opportunity to predict the properties of new compounds before produced in the laboratory. In this study, the valence electron concentration (VEC), atomic size difference (δ), enthalpy of mixing (∆????), the entropy of mixing (∆????) and electronegativity difference (∆χ) values are calculated for each alloy and a dataset has been created. We use gradient boosted trees machine learning method with TensorFlow artificial intelligence program to explore phase selection using an experimental dataset consisting of 118 multi-component alloy system. We divide the whole dataset into two portions with training and evaluate dataset. The training dataset contains 73 and evaluate dataset contains 45 multi-component alloy systems. We also show three of the predicted multi-component alloy system to examine which physical values are used predominantly during prediction. We look at the Receiver Operating Characteristic (ROC) of the results, which will give us a better idea of the tradeoff between the true positive rate and false positive rate. It has been observed that this learning method predicts the structure correctly in 95% of the results with limited data.

PDF

___

[1] P. F. Pagoria, G. S. Lee, A. R. Mitchell, and R. D. Schmidt, ‘A review of energetic materials synthesis’, Thermochim. Acta, vol. 384, no. 1–2, pp. 187–204, 2002.
[2] M. H. Phan and S. C. Yu, ‘Review of the magnetocaloric effect in manganite materials’, J. Magn. Magn. Mater., vol. 308, no. 2, pp. 325–340, 2007.
[3] M. N. Gueye, A. Carella, J. Faure-Vincent, R. Demadrille, and J. P. Simonato, ‘Progress in understanding structure and transport properties of PEDOT-based materials: A critical review’, Prog. Mater. Sci., vol. 108, no. November 2019, p. 100616, 2020.
[4] R. Shan, J. Han, J. Gu, H. Yuan, B. Luo, and Y. Chen, ‘A review of recent developments in catalytic applications of biochar-based materials’, Resour. Conserv. Recycl., vol. 162, no. June, p. 105036, 2020.
[5] E. Glikson and A. W. Woolley, ‘Human trust in artificial intelligence: Review of empirical research’, Acad. Manag. Ann., vol. 14, no. 2, pp. 627–660, 2020.
[6] C. Chen, Y. Zuo, W. Ye, X. Li, Z. Deng, and S. P. Ong, ‘A Critical Review of Machine Learning of Energy Materials’, Adv. Energy Mater., vol. 10, no. 8, pp. 1–36, 2020.
[7] S. Lalmuanawma, J. Hussain, and L. Chhakchhuak, ‘Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review’, Chaos, Solitons and Fractals, vol. 139, p. 110059, 2020.
[8] I. Z. A. D. P. No and W. Naudé, ‘DISCUSSION PAPER SERIES Artificial Intelligence against COVID-19 : An Early Review Artificial Intelligence against COVID-19 : An Early Review’, no. 13110, 2020.
[9] ‘Abadi, Mart'in, Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J, "Tensorflow: A system for large-scale machine learning." In 12th $USENIX$ Symposium on Operating Systems Design and Implementation ($OSDI$ 16) pp. 265–283, 2016
[10] R. Feng, P. K. Liaw, M. C. Gao, and M. Widom, ‘First-principles prediction of high-entropy-alloy stability’, npj Comput. Mater., vol. 3, no. 1, p. 50, Dec. 2017.
[11] M. Rupp, A. Tkatchenko, K. R. Müller, and O. A. Von Lilienfeld, ‘Fast and accurate modeling of molecular atomization energies with machine learning’, Phys. Rev. Lett., vol. 108, no. 5, pp. 1–5, 2012.
[12] Y. M. Zhang, S. Yang, and J. R. G. Evans, ‘Revisiting Hume-Rothery’s Rules with artificial neural networks’, Acta Mater., vol. 56, no. 5, pp. 1094–1105, 2008.
[13] L. Ward, A. Agrawal, A. Choudhary, and C. Wolverton, ‘A general-purpose machine learning framework for predicting properties of inorganic materials’, npj Comput. Mater., vol. 2, no. July, pp. 1–7, 2016.
[14] B. Meredig and C. Wolverton, ‘A hybrid computational-experimental approach for automated crystal structure solution’, Nat. Mater., vol. 12, no. 2, pp. 123–127, 2013.
[15] S. Guo and C. T. Liu, ‘Phase stability in high entropy alloys: Formation of solid-solution phase or amorphous phase’, Prog. Nat. Sci. Mater. Int., vol. 21, no. 6, pp. 433–446, 2011.
[16] S. Guo, C. Ng, J. Lu, and C. T. Liu, ‘Effect of valence electron concentration on stability of fcc or bcc phase in high entropy alloys’, J. Appl. Phys., vol. 109, no. 10, 2011.
[17] S. Guo, ‘Phase selection rules for cast high entropy alloys: an overview’, Mater. Sci. Technol., vol. 31, no. 10, pp. 1223–1230, 2015.
[18] Z. Wang, S. Guo, and C. T. Liu, ‘Phase Selection in High-Entropy Alloys: From Nonequilibrium to Equilibrium’, JOM, vol. 66, no. 10, pp. 1966–1972, Oct. 2014.
[19] A. Takeuchi and A. Inoue, ‘Classification of Bulk Metallic Glasses by Atomic Size Difference, Heat of Mixing and Period of Constituent Elements and Its Application to Characterization of the Main Alloying Element’, Mater. Trans., vol. 46, no. 12, pp. 2817–2829, 2006.
[20] G. Ke et al., ‘LightGBM: A highly efficient gradient boosting decision tree’, Adv. Neural Inf. Process. Syst., vol. 2017-Decem, no. Nips, pp. 3147–3155, 2017.
[21] A. J. Ferreira and M. A. T. Figueiredo, ‘Boosting Algorithms: A Review of Methods, Theory, and Applications’, in Ensemble Machine Learning: Methods and Applications, C. Zhang and Y. Ma, Eds. Boston, MA: Springer US, 2012, pp. 35–85.