Machine Learning-Driven Density Prediction for Nanomaterials

Shams Ansaf

doi:10.31185/wjps.538

Authors

Shams Ansaf Islamic Azad University Kermanshah Branch, IRAN

DOI:

https://doi.org/10.31185/wjps.538

Keywords:

Nanomaterial, Machine Learning, Chemical Composition

Abstract

This paper provides a machine learning method that uses band gap and chemical composition data from the Materials Project database to predict the density of nanomaterials. We developed an improved Random Forest Regressor and compared it against several baseline models, including Linear regression, to demonstrate the superior performance of our approach Using a rigorous preprocessing procedure, we combined elemental characteristics extracted from chemical formulae with band gap data. To improve the random forest hyperparameters and boost the predictive power of the model, we employed grid search cross-validation. Key components that have the biggest effects on nanomaterial density were identified via feature importance analysis. Insights into structure-property correlations in nanomaterials were gained by examining the link between density and band gap. Because machine learning allows for quick density estimates, this work shows how it can speed up the discovery and design of nanomaterials. By enabling high-throughput screening of nanomaterials and directing experimental efforts in materials synthesis and characterization, the created model can be a useful tool for nanotechnology researchers and engineers.

References

Achar, Siddarth K, Bernasconi, Leonardo, & Johnson, J Karl. (2023). Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic Positions. Nanomaterials, 13(12), 1853. MDPI.

Huo, Sitong, Zhang, Shuqing, Wu, Qilin, & Zhang, Xinping. (2024). Feature-Assisted Machine Learning for Predicting Band Gaps of Binary Semiconductors. Nanomaterials, 14(5), 445. Multidisciplinary Digital Publishing Institute.

Cai, Jiazhen, Chu, Xuan, Xu, Kun, Li, Hongbo, & Wei, Jing. (2020). Machine learning-driven new material discovery. Nanoscale Advances, 2(8), 3115-3130. Royal Society of Chemistry.

Gor, Meet, Dobriyal, Aashutosh, Wankhede, Vishal, Sahlot, Pankaj, Grzelak, Krzysztof, Kluczyński, Janusz, & Łuszczek, Jakub. (2022). Density prediction in powder bed fusion additive manufacturing: machine learning-based techniques. Applied Sciences, 12(14), 7271. MDPI.

Hu, Rui, Lei, Wen, Yuan, Hongmei, Han, Shihao, & Liu, Huijun. (2022). High-throughput prediction of the band gaps of van der Waals heterostructures via machine learning. Nanomaterials, 12(13), 2301. MDPI.

Schmidt, Jonathan, Shi, Jingming, Borlido, Pedro, Chen, Liming, Botti, Silvana, & Marques, Miguel AL. (2017). Predicting the thermodynamic stability of solids combining density functional theory and machine learning. Chemistry of Materials, 29(12), 5090-5103. ACS Publications.

Jha, Dipendra, Choudhary, Kamal, Tavazza, Francesca, Liao, Wei-keng, Choudhary, Alok, Campbell, Carelyn, & Agrawal, Ankit. (2019). Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nature Communications, 10(1), 5316. Nature Publishing Group UK London.

Liu, Yue, Zhao, Tianlu, Ju, Wangwei, & Shi, Siqi. (2017). Materials discovery and design using machine learning. Journal of Materiomics, 3(3), 159-177. Elsevier.

Louis, Steph-Yves, Zhao, Yong, Nasiri, Alireza, Wang, Xiran, Song, Yuqi, Liu, Fei, & Hu, Jianjun. (2020). Graph convolutional neural networks with global attention for improved materials property prediction. Physical Chemistry Chemical Physics, 22(32), 18141-18148. Royal Society of Chemistry.

Van Roekeghem, Ambroise, Carrete, Jesús, Oses, Corey, Curtarolo, Stefano, & Mingo, Natalio. (2016). High-throughput computation of thermal conductivity of high-temperature solid phases: the case of oxide and fluoride perovskites. Physical Review X, 6(4), 041061. APS.

Iqbal, Touseef, & Qureshi, Shaima. (2022). The survey: Text generation models in deep learning. Journal of King Saud University-Computer and Information Sciences, 34(6), 2515-2528. Elsevier.

Mukhamedov, BO, Karavaev, KV, & Abrikosov, IA. (2021). Machine learning prediction of thermodynamic and mechanical properties of multicomponent Fe-Cr-based alloys. Physical Review Materials, 5(10), 104407. APS.

Yang, Ming, Zhang, Xingli, & Zhang, Hang. (2021). Effects of monovacancy on thermal properties of bilayer graphene nanoribbons by molecular dynamics simulations. Journal of Thermal Science, 1-8. Springer.

Jain, Anubhav, Hautier, Geoffroy, Ong, Shyue Ping, & Persson, Kristin. (2016). New opportunities for materials informatics: resources and data mining techniques for uncovering hidden relationships. Journal of Materials Research, 31(8), 977-994. Cambridge University Press.

Fu, Ziyang, Liu, Weiyi, Huang, Chen, & Mei, Tao. (2022). A review of performance prediction based on machine learning in materials science. Nanomaterials, 12(17), 2957. MDPI.

Jain, Anubhav, Ong, Shyue Ping, Hautier, Geoffroy, Chen, Wei, Richards, William Davidson, Dacek, Stephen, Cholia, Shreyas, Gunter, Dan, Skinner, David, Ceder, Gerbrand, et al. (2013). Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1). AIP Publishing.

Pedregosa, Fabian. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825.

Hastie, Trevor, Tibshirani, Robert, & Friedman, Jerome H. (2009). The elements of statistical learning: data mining, inference, and prediction. 2nd ed. Springer.

Breiman, Leo. (2001). Random forests. Machine Learning, 45, 5-32. Springer.