Машинне навчання в термоелектричному матеріалознавстві
Ключові слова:
методи машинного навчання, термоелектричне матеріалознавствоАнотація
У роботі наводяться методи машинного навчання та їхнє застосування в термоелектричному матеріалознавстві. Показано результати їхнього застосування, сильні сторони та області застосування. Було взято до уваги складнощі, які виникають у процесі прогнозування властивостей термоелектричних матеріалів та способи їх подолання.
The paper presents machine learning methods and their application in thermoelectric materials science. The results of their application, strong points and application areas are shown. The difficulties that arise in the process of predicting the properties of thermoelectric materials and ways to overcome them were taken into account. Bibl. 30, Fig. 1, Tabl. 2.
Посилання
1. Liu Y., Zhao T., Ju W., & Shi S. (2017). Materials discovery and design using machine learning. Journal of Materiomics, 3 (3), 159 – 177. Elsevier BV. https://doi.org/10.1016/j.jmat.2017.08.002
2. Juan Y., Dai Y., Yang Y., & Zhang J. (2021). Accelerating materials discovery using machine learning. Journal of Materials Science & Technology, 79, 178 – 190. Elsevier BV. https://doi.org/10.1016/j.jmst.2020.12.010
3. von Lilienfeld O.A. & Burke K. (2020). Retrospective on a decade of machine learning for chemical discovery. Nature Communications, 11 (1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41467-020-18556-9
4. Kim E., Huang K., Saunders A., McCallum A., Ceder G. & Olivett, E. (2017). Materials synthesis insights from scientific literature via text extraction and machine learning. Chemistry of Materials, 29 (21), 9436 – 9444). American Chemical Society (ACS). https://doi.org/10.1021/acs.chemmater.7b03500
5. Cao B., Adutwum L.A., Oliynyk A.O., Luber E.J., Olsen B.C., Mar A. & Buriak J.M. (2018). How to optimize materials and devices via design of experiments and machine learning: demonstration using organic photovoltaics. ACS Nano, 12 (8), 7434 – 7444. American Chemical Society (ACS). https://doi.org/10.1021/acsnano.8b04726
6. Queen H.J., J.J., D.T.J., K.V.S. Moses Babu and Thota S.P. (1021), Machine learning-based predictive techno-economic analysis of power system, IEEE Access, 9, 123504 – 123516, 2021, doi: 10.1109/ACCESS.2021.3110774.
7. Mahesh B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR), 9 (1), 381 – 386.
8. Singh A., Thakur N. and Sharma A. A review of supervised machine learning algorithms (2016). 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 2016, pp. 1310 – 1315.
9. Alloghani M., Al-Jumeily D., Mustafina J., Hussain A. & Aljaaf A.J. (2019). A systematic review on supervised and unsupervised machine learning algorithms for data science. Unsupervised and Semi-Supervised Learning (pp. 3 – 21). Springer International Publishing. https://doi.org/10.1007/978-3-030-22475-2_1
10. Gaultois M.W., Oliynyk A.O., Mar A., Sparks T.D., Mulholland G.J. & Meredig, B. (2016). Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties. APL Materials, 4 (5). AIP Publishing. https://doi.org/10.1063/1.4952607
11. Choudhary K., Garrity K. F., & Tavazza F. (2020). Data-driven discovery of 3D and 2D thermoelectric materials. Journal of Physics: Condensed Matter, 32 (47), 475501). IOP Publishing. https://doi.org/10.1088/1361-648x/aba06b
12. Somvanshi M., Chavan P., Tambade S. & Shinde S.V. (2016). A review of machine learning techniques using decision tree and support vector machine. 2016 International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE. https://doi.org/10.1109/iccubea.2016.7860040
13. Alrebdi T.A., Wudil Y.S., Ahmad U.F., Yakasai F.A., Mohammed J. & Kallas F.H. (2022). Predicting the thermal conductivity of Bi2Te3-based thermoelectric energy materials: A machine learning approach. International Journal of Thermal Sciences, 181, 107784. Elsevier BV. https://doi.org/10.1016/j.ijthermalsci.2022.107784
14. Chen D., Jiang F., Fang L., Zhu Y.-B., Ye C.-C. & Liu W.-S. (2022). Machine learning assisted discovering of new M2X3-type thermoelectric materials. Rare Metals, 41 (5), 1543 – 1553. Springer Science and Business Media LLC. https://doi.org/10.1007/s12598-021-01911-0
15. Sheng Y., Wu Y., Yang J., Lu W., Villars P. & Zhang W. (2020). Active learning for the power factor prediction in diamond-like thermoelectric materials. Computational Materials, 6 (1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41524-020-00439-8
16. Na G.S. & Chang H. (2022). A public database of thermoelectric materials and system-identified material representation for data-driven discovery. Computational Materials, 8 (1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41524-022-00897-2
17. Uysal F., Kilinc E., Kurt H., Celik E., Dugenci M. & Sagiroglu S. (2017). Estimating Seebeck coefficient of a p-type high temperature thermoelectric material using bee algorithm multi-layer perception. Journal of Electronic Materials, 46 (8), 4931 – 4938). Springer Science and Business Media LLC. https://doi.org/10.1007/s11664-017-5497-6
18. Jia X., Deng Y., Bao, X, Yao H., Li S., Li Z., Chen C., Wang X., Mao J., Cao F., Sui J., Wu J., Wang C., Zhang Q. & Liu X. (2022). Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials. Computational Materials, 8 (1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41524-022-00723-9
19. Iwasaki Y., Sawada R., Stanev V., Ishida M., Kirihara A., Omori Y., Someya H., Takeuchi I., Saitoh E., & Yorozu S. (2019). Identification of advanced spin-driven thermoelectric materials via interpretable machine learning. Computational Materials, 5 (1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41524-019-0241-9
20. Sheng Y., Deng T., Qiu P., Shi X., Xi J., Han Y. & Yang J. (2021). Accelerating the discovery of Cu–Sn–S thermoelectric compounds via high-throughput synthesis, characterization, and machine learning-assisted image analysis. Chemistry of Materials, 33 (17), 6918 – 6924). American Chemical Society (ACS). https://doi.org/10.1021/acs.chemmater.1c01856
21. Shimizu N. & Kaneko H. (2020). Direct inverse analysis based on Gaussian mixture regression for multiple objective variables in material design. Materials & Design, 196, 109168. Elsevier BV. https://doi.org/10.1016/j.matdes.2020.109168
22. Antunes L.M., Vikram Plata J.J., Powell A.V., Butler K.T. & Grau-Crespo R. (2022). Machine learning approaches for accelerating the discovery of thermoelectric materials. ACS Symposium Series (pp. 1 – 32). American Chemical Society. https://doi.org/10.1021/bk-2022-1416.ch001
23. Guo Q., Chan M., Kuropatwa B.A., & Kleinke H. (2013). Enhanced thermoelectric properties of variants of Tl9SbTe6 and Tl9BiTe6. Chemistry of Materials, 25 (20), 4097 – 4104). American Chemical Society (ACS). https://doi.org/10.1021/cm402593f
24. Parker D., & Singh D.J. (2013). Alkaline earth lead and tin compounds Ae2Pb, Ae2Sn, Ae = Ca, Sr, Ba, as thermoelectric materials. Science and Technology of Advanced Materials, 14 (5), 055003. Informa UK Limited. https://doi.org/10.1088/1468-6996/14/5/055003
25. Sun P., Oeschler N., Johnsen S., Iversen B. B. and Steglich F. (2009). Huge thermoelectric power factor: FeSb2 versus FeAs2 and RuSb2, Appl. Phys. Express 2, 091102.
26. Iwasaki Y., Takeuchi I., Stanev V., Kusne A. G., Ishida M., Kirihara A., Ihara K., Sawada R., Terashima K., Someya H., Uchida K., Saitoh E, & Yorozu S. (2019). Machine-learning guided discovery of a new thermoelectric material. Scientific Reports, 9(1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41598-019-39278-z
27. Jaafreh R., Yoo Seong, K., Kim J.-G. & Hamad K. (2022). A deep learning perspective into the figure-of-merit of thermoelectric materials. Materials Letters, 319, 132299. Elsevier BV. https://doi.org/10.1016/j.matlet.2022.132299
28. Li Y., Zhang J., Zhang K., Zhao M., Hu K. & Lin X. (2022). Large data set-driven machine learning models for accurate prediction of the thermoelectric figure of merit. ACS Applied Materials & Interfaces, 14 (50), 55517 – 55527). American Chemical Society (ACS). https://doi.org/10.1021/acsami.2c15396
29. Wang T., Zhang C., Snoussi H. & Zhang G. (2019). Machine learning approaches for thermoelectric materials research. Advanced Functional Materials, 1906041. https://doi.org/10.1002/adfm.201906041
30. Na G.S., Chang H. (2022). A public database of thermoelectric materials and system-identified material representation for data-driven discovery. Comput Mater 8 (1), 214. Springer Science and Business Media LLC https://doi.org/10.1038/s41524-022-00897-2.
