Programme

P.65 -- Poster, VCTP-48

Date: Thursday, 3 August 2023

Time: 08:30 - 10:00

Studying and predicting Energy Gap of materials by Machine Learning Method

Le Huu Nghia (1) and Nguyen Thanh Tien (2)

(1) School of Graduate, Can Tho University, CanTho, Vietnam. (2) College of Natural Sciences, Can Tho University.

Today, big data and artificial intelligence are revolutionizing many areas of our lives and the sciences. Materials science is not an exception, data-driven materials science is becoming the fourth paradigm of materials research, this report uses the Machine Learning method to find the best high-performance energy band gap in materials data. In this work, we start with data processing, analysis, modeling, and visualization of the results as well as a data organization process for machine learning models in materials research. We change the percentage of the dataset by setting a random seed from 0 to 60 (random. seed()) and find the best percentage for the data with train split = 70%; test split = 10%, and validation split = 20%. From the above percentage, our supervised machine learning finds the best-performing model: ExtraTreesRegressor() with the parameters: Coefficient of determination (R2): 0.7479, Mean Absolute Error (MAE): 0.5844, Root Mean Squared Error (RMSE): 0.8088. We consider therefore the different facets of interpretability prediction of models of machine learning and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science. References: [1] Anthony Yu-Tung Wang., Ryan J. Murdock, Steven K. Kauwe., et al. Machine Learning for Materials Scientists: An introductory guide towards best practices. Chemistry of Materials. 2022. DOI: 10.1021/acs.chemmater.0c01907. [2] Jason M. Crowley., Jamil Tahir-Kheli., and William A. Goddard., Resolution of the Band Gap Prediction Problem for Materials Design. The Journal of Physical Chemistry Letters. 2016. DOI: 10.1021/acs.jpclett.5b02870. [3] Mansouri Tehrani, A., Oliynyk, A. O., Parry, M., et al. Machine Learning Directed Search for Ultraincompressible, Superhard Materials. Journal of the American Chemical Society. 2018. DOI: https://doi.org/10.1021/jacs.8b02717. [4] Schmidt, J., Marques, M. R. G., Botti, S., Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. Computational Materials. 2019. DOI: https://doi.org/10.1038/s41524-022-00734-6. [5] Jing Wei., Xuan Chu., Xiang-Yu Sun., Kun Xu., et al. Machine learning in materials science. WILEY. 2019. DOI: 10.1002/inf2.12028. [6] Lauri Himanen., Marc O.J. Jäger., Eiaki V. Morooka., DScribe: Library of descriptors for machine learning in materials science. ScienceDirect. 2019. DOI: https://doi.org/10.1016/j.cpc.2019.106949.

Presenter: Le Huu Nghia