The traditional methods employed in the investigation of new materials, specifically the empirical and density functional theory (DFT) approaches, are insufficient to satisfy the requirements of modern materials science. This can be attributed to the prolonged development cycles, suboptimal efficiency, and exorbitant costs. The utilization of machine learning (ML) is a common practice in material detection, analysis, and design owing to its exceptional predictive capabilities, efficient data processing, and swift development cycle. This can be attributed to its relatively low computational expense. This paper provides an analysis of the essential operational procedures that are involved in the analysis of material properties using ML techniques. Furthermore, the present study provides a summary of the recent utilization of ML algorithms in diverse established domains of materials science, along with a discussion on the requisite improvements for their widespread implementation. The utilization of ML has been widely implemented in various fields of materials science. This paper offers an academic analysis of the paradigms of ML in the context of materials science. The article provides a clear and comprehensive overview of the essential steps involved in data processing, which encompass sample construction, data modelling, and model evaluation. The present manuscript presents a comprehensive survey of the application of ML methodologies in the domain of material science.
Keywords
Materials Science, Machine Learning, Artificial Intelligence, Material Analysis, ML Algorithms.
K. Omri, S. Gouadria, M. Madani, S. Mnefgui, N. Alonizan, and F. Alharbi, “Correction to: Doping effects of Ca2+ on the optical and dielectric properties of Ca/ZnO nanopowders materials,” J. Mater. Sci.: Mater. Electron., vol. 34, no. 8, 2023.
J. H. Kim, T. Y. Shin, S. Yekaterina, and C. K. Park, “Data science approach to find an outlier in the group of cement dispersants,” Constr. Build. Mater., vol. 368, no. 130347, p. 130347, 2023.
M. R. Raymond, “Assessing performance: Designing, scoring, and validating performance tasksâ..by Johnson, R. l., penny, J. a., & Gordon, B,” J. Educ. Meas., vol. 46, no. 4, pp. 474–477, 2009.
S. R. Mohan, S. K. Neogy, A. Seth, N. K. Garg, and S. Mittal, “An optimization model to determine master designs and runs for advertisement printing,” J. Math. Model. Algorithms, vol. 6, no. 2, pp. 259–271, 2007.
M. Kuanr and P. Mohapatra, “Outranking relations based multi-criteria recommender system for analysis of health risk using multi-objective feature selection approach,” Data Knowl. Eng., vol. 145, no. 102144, p. 102144, 2023.
M. Büchi, N. Festic, and M. Latzer, “The chilling effects of digital dataveillance: A theoretical model and an empirical research agenda,” Big Data Soc., vol. 9, no. 1, p. 205395172110653, 2022.
P. R. Punnam, A. Dutta, B. Krishnamurthy, and V. K. Surasani, “Study on utilization of machine learning techniques for geological CO2 sequestration simulations,” Mater. Today, vol. 72, pp. 378–385, 2023.
K. ur Rehman, J. Li, Y. Pei, and A. Yasin, “A review on machine learning techniques for the assessment of image grading in breast mammogram,” Int. J. Mach. Learn. Cybern., vol. 13, no. 9, pp. 2609–2635, 2022.
Z. U. Khan, F. Ali, I. Ahmad, M. Hayat, and D. Pi, “iPredCNC: Computational prediction model for cancerlectins and non-cancerlectins using novel cascade features subset selection,” Chemometr. Intell. Lab. Syst., vol. 195, no. 103876, p. 103876, 2019.
N. Hikichi et al., “Role of sodium cation during aging process in the synthesis of LEV-type zeolite,” Microporous Mesoporous Mater., vol. 284, pp. 82–89, 2019.
H. Gao et al., “Investigation of contact electrification between 2D MXenes and MoS 2 through density functional theory and triboelectric probes,” Adv. Funct. Mater., vol. 33, no. 15, p. 2213410, 2023.
T.-Y. Huang, “General framework, mathematical model, current activities and open issues for media adaptation in MPEG-21 DIA: General framework, mathematical model, current activities and open issues for media adaptation in MPEG-21 DIA,” Jisuanji Xuebao (Chin. J. Comput.), vol. 31, no. 7, pp. 1185–1199, 2009.
E. Tartaglione, A. Bragagnolo, F. Odierna, A. Fiandrotti, and M. Grangetto, “SeReNe: Sensitivity-based regularization of neurons for structured sparsity in neural networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 12, pp. 7237–7250, 2022.
G. Wu, “Deep reinforcement learning based multi-layered traffic scheduling scheme in data center networks,” Wirel. Netw., 2022.
I. Rezaei, S. H. Amirshahi, and A. A. Mahbadi, “Utilizing support vector and kernel ridge regression methods in spectral reconstruction,” Results in Optics, vol. 11, no. 100405, p. 100405, 2023.
O. Telschow et al., “Preserving the stoichiometry of triple-cation perovskites by carrier-gas-free antisolvent spraying,” J. Mater. Chem. A Mater. Energy Sustain., vol. 10, no. 37, pp. 19743–19749, 2022.
P. R. Dos Santos, C. T. Pich, D. Back, F. Smiderle, F. Dumas, and S. Moura, “Synthesis, chemical characterization and DNA interaction study of new diclofenac and ibuprofen zinc (II)-nicotinamide ternary complexes as cyclooxygenase inhibitor prototypes,” J. Inorg. Biochem., vol. 206, no. 111046, p. 111046, 2020.
M. Baumgartner, M. Kropf, L. Haider, S. Veeranki, D. Hayn, and G. Schreier, “ECG classification combining conventional signal analysis, random forests and neural networks - a stacked learning scheme,” in 2021 Computing in Cardiology (CinC), 2021.
Z. Xia, Q. Zhang, M. Yuan, Z. Liu, and X. Ma, “Structural, magnetic and transport properties of the quaternary Heusler alloy CoFeMnSn,” J. Alloys Compd., vol. 937, no. 168497, p. 168497, 2023.
S.-P. Korhonen, K. Tuppurainen, A. Asikainen, R. Laatikainen, and M. Peräkylä, “SOMFA on large diverse xenoestrogen dataset: The effect of superposition algorithms and external regression tools,” QSAR Comb. Sci., vol. 26, no. 7, pp. 809–819, 2007.
S. Dey, A. Das Sharma, and J. Mukhopadhyay, “Effect of oxygen non-stoichiometry and redox phenomena in La/Ba–Sr–Co–Fe–O-based perovskite systems and its heterostructure as applicable in Solid Oxide Cell (SOC) air electrode,” Ceram. Int., 2022.
W. T. M. Van Gompel et al., “Study on the dynamics of phase formation and degradation of 2D layered hybrid perovskites and low‐dimensional hybrids containing mono‐functionalized oligothiophene cations,” ChemNanoMat, vol. 7, no. 9, pp. 1013–1019, 2021.
L. Shen et al., “Three- and two-dimensional mixed metal halide perovskites for high-performance photovoltaics,” Org. Electron., vol. 118, no. 106796, p. 106796, 2023.
M. Beglari, N. Goudarzi, D. Shahsavani, M. Arab Chamjangali, and Z. Mozafari, “Combination of radial distribution functions as structural descriptors with ligand-receptor interaction information in the QSAR study of some 4-anilinoquinazoline derivatives as potent EGFR inhibitors,” Struct. Chem., vol. 31, no. 4, pp. 1481–1491, 2020.
X. Chen, J. Qiao, Z. Wang, W. Sun, and K. Sun, “Layered perovskites with exsolved Co-Fe nanoalloy as highly active and stable anodes for direct carbon solid oxide fuel cells,” J. Alloys Compd., vol. 940, no. 168872, p. 168872, 2023.
A. A. Belik, “Comments on the paper ‘Influence of Ba-doping on the structural and physical properties of Sr2−Ba FeVO6 double perovskites,’” J. Alloys Compd., vol. 940, no. 168932, p. 168932, 2023.
S. Cheng, Z. Wang, B. Yang, and K. Nakano, “Convolutional neural network-based Lane-change strategy via motion image representation for automated and connected vehicles,” IEEE Trans. Neural Netw. Learn. Syst., vol. PP, 2023.
R. Gurnani, C. Kuenneth, A. Toland, and R. Ramprasad, “Polymer informatics at scale with multitask graph neural networks,” Chem. Mater., vol. 35, no. 4, pp. 1560–1567, 2023.
X. Li and Y. Cheng, “Understanding the message passing in graph neural networks via power iteration clustering,” Neural Netw., vol. 140, pp. 130–135, 2021.
R. Zhuang et al., “Effect of molecular configuration of additives on perovskite crystallization and hot carriers behavior in perovskite solar cells,” Chem. Eng. J., vol. 463, no. 142449, p. 142449, 2023.
C. Gao, H. Lin, and H. Hu, “Forest-fire-risk prediction based on random forest and backpropagation neural network of Heihe area in Heilongjiang Province, China,” Forests, vol. 14, no. 2, p. 170, 2023.
Q. Gu, J. Ji, F. Guo, H. Chen, T. Yang, and X. Tan, “First principle study on the electronic, magnetic and phase stability of the full-Heusler compound Fe2CuSi,” SPIN, vol. 11, no. 01, p. 2150001, 2021.
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Chinua Obasi
University of Nigeria, Nsukka 410105, Enugu, Nigeria.
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Cite this article
Chinua Obasi and Oluyemi Oranu, “Exploring Machine Learning Algorithms and Their Applications in Materials Science”, Journal of Computational Intelligence in Materials Science, vol.2, pp. 023-035, 2024. doi: 10.53759/832X/JCIMS202402003.
