Abstract
Knowledge of the mechanical properties of structural materials is essential for their practical applications. In the present work, three-hundred and sixty data samples on four mechanical properties of steels—fatigue strength, tensile strength, fracture strength and hardness—were selected from the Japan National Institute of Material Science database, comprising data on carbon steels and low-alloy steels. Five machine learning algorithms were used to predict the mechanical properties of the materials represented by the three-hundred and sixty data samples, and random forest regression showed the best predictive performance. Feature selection conducted by random forest and symbolic regressions revealed the four most important features that most influence the mechanical properties of steels: the tempering temperature of steel, and the alloying elements of carbon, chromium and molybdenum. Mathematical expressions were generated via symbolic regression, and the expressions explicitly predicted how each of the four mechanical properties varied quantitatively with the four most important features. This study demonstrates the great potential of symbolic regression in the discovery of novel advanced materials.
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References
Ramprasad R, Batra R, Pilania G, et al. Machine learning in materials informatics: Recent applications and prospects, npj Comput Mater, 2017, 3: 54
Xue D, Xue D, Yuan R, et al. An informatics approach to transformation temperatures of NiTi-based shape memory alloys. Acta Mater, 2017, 125: 532–541
Ward L, Agrawal A, Choudhary A, et al. A general-purpose machine learning framework for predicting properties of inorganic materials, npj Comput Mater, 2016, 2: 1–7
Yosipof A, Nahum O E, Anderson A Y, et al. Data mining and machine learning tools for combinatorial material science of all-oxide photovoltaic cells. Mol Inf, 2015, 34: 367–379
Agrawal A, Choudhary A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Mater, 2016, 4: 053208
Xiong J, Shi S Q, Zhang T Y. A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys. Mater Des, 2020, 187: 108378
Takahashi K, Tanaka Y. Material synthesis and design from first principle calculations and machine learning. Comput Mater Sci, 2016, 112: 364–367
Hill J, Mulholland G, Persson K, et al. Materials science with large-scale data and informatics: Unlocking new opportunities. MRS Bull, 2016, 41: 399–409
Green M L. Choi C L, Hattrick-Simpers J R, et al. Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies. Appl Phys Rev, 2017, 4: 011105
Huber L, Hadian R, Grabowski B, et al. A machine learning approach to model solute grain boundary segregation, npj Comput Mater, 2018, 4: 64
Zhu Q, Samanta A, Li B, et al. Predicting phase behavior of grain boundaries with evolutionary search and machine learning. Nat Commun, 2018, 9: 467
Raccuglia P, Elbert K C, Adler P D F, et al. Machine-learning-assisted materials discovery using failed experiments. Nature, 2016, 533: 73–76
Agrawal A, Deshpande P D, Cecen A, et al. Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. Integrating Mater, 2014, 3: 90–108
Agrawal A, Choudhary A. An online tool for predicting fatigue strength of steel alloys based on ensemble data mining. Int J Fatigue, 2018, 113: 389–400
Yamazaki M, Xu Y, Murata M, et al. NIMS structural materials databases and cross search engine - MatNavi. VTT Symp, 2007
Lison P. An introduction to machine learning. Language Technology Group: Edinburgh, UK, 2015
Hall M, Frank E, Holmes G, et al. The WEKA data mining software. SIGKDD Explor Newsl, 2009, 11: 10–18
Wagner S, Affenzeller M. HeuristicLab: A generic and extensible optimization environment. Adapt Nat Comput Algorithms. Vienna: Springer, 2005. 538–541
Louppe G, Wehenkel L, Sutera A, et al. Understanding variable importances in forests of randomized trees. Adv Neural Inf Process Syst, 2013. 431–439
Vladislavleva K, Veeramachaneni K, Burland M, et al. Knowledge mining with genetic programming methods for variable selection in flavor design. In: Proc 12th Annu Genet Evol Comput Conf, GECCO 2010. 941–948
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This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFB0704404), the Hong Kong Polytechnic University (Internal Grant Nos. 1-ZE8R and G-YBDH) and the 111 Project of the State Administration of Foreign Experts Affairs and the Ministry of Education, China (Grant No. D16002).
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Xiong, J., Zhang, T. & Shi, S. Machine learning of mechanical properties of steels. Sci. China Technol. Sci. 63, 1247–1255 (2020). https://doi.org/10.1007/s11431-020-1599-5
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DOI: https://doi.org/10.1007/s11431-020-1599-5