Industry 4.0
Vol. 3 (2018), Issue 4, pg(s) 186-189

Machine learning applications for design of new materials: A review

  • 1 College of Information Business Systems – National University of Science and Technology, MISiS Leninsky Ave. 4, 119049, Moscow, Russian Federation

Abstract

The importance of materials in our life is well known to the world. Material science is one of the crucial research domain which deals with planning, development, synthesis and analysis of materials and its use in the real applications. The scientists and researcher are using highly advance technologies for the production and design of new materials in present era; however the process is time consuming. Machine Learning can be defined as a set of techniques that learn from the large amount of available data and make predictions for the new data. Recently, machine learning has gained a great attraction in material science research and has been used in several research studies. Machine learning techniques have the potential to learn from the large amount of materials data and make predictions about different properties for new materials of good quality. These predictions can help in designing new materials. In this paper, we present a comprehensive review on the machine learning applications in different material science domain. We believe that the paper would be useful for the researchers, academicians and students who are involved in the design of new materials.

Keywords

References

  1. About J., Materials genome initiative for global competitiveness, National Science and Technology Council, USA, 2011.
  2. Hohenberg P., W. Kohn, Inhomogeneous electron gas, Phys Rev, 136, 1964, 864-871
  3. Binder K., A. Baumgartner, The Monte Carlo method in condensed matter physics, 71, 1995.
  4. Alder B. J., T. E. Wainwright, Studies in molecular dynamics. I. General method, J Chem Phys, 31 (2), 1959, 459-466.
  5. Rajan K., Materials informatics, Mater TodCay, 8, 2005, pp. 38-45.
  6. Hautier G., A. Jain, S. P. Ong, From the computer to the laboratory: materials discovery and design using firstprinciples calculations, J mater. Sci, 47, 2012, 7317-7340.
  7. Yu X. L., B. Yi, X. Y. Wang, Prediction of the glass transition temperature for polymers with artificial neural network, J Theor Comput Chem, 7, 2008, 953-963.
  8. Materials Genome Initiative: https://www.mgi.gov accessed on 30.07.2018.
  9. Belsky A., M. Hellenbrandt, V. L. Karen, P. Luksch, New developments in the inorganic crystal structure database (ICSD): accessibility in support of materials research and design, Acta Crystallogr, Sect B Struct Sci, 58, 2002, 364- 369
  10. Super Conducting Critical Temperatures (SuperCon): https://supercon.nims.go.jp accessed on 30.07.18
  11. Kirkli S., J. E. Saal, B. Meredig, A. Thompson, J. W. Doak, M. Aykol, The open quantum materials database (OQMD): assessing the accuracy of DFT formation energies, npj Comput Mater, 1, 2015, 15010.
  12. Mueller T., A.G. Kusne, R. Ramprasad, Machine learning in materials science: recent progress and emerging applications, Rev Comput Chem, 29, 2016, 186.
  13. Yue L., Z. Tianlu, J. Wangwei, S. Siqi, Materials discovery and design using machine learning, J Materiomics, 3, 2017, 159-177.
  14. Ning X., M. Walters, G. Karypisxy, Improved machine learning models for predicting selective compounds, J Chem Inf Model, 52, 2012, 38-50.
  15. Bertinetto C., C. Duce, A. Micheli, R. Solaro, A. Starita, M. R. Tine, Evaluation of hierarchical structured representations for QSPR studies of small molecules and polymers by recursive neural networks, J Mol Graph Model, 27, 2009, 797-802.
  16. Guo Z., S. Malinov, W. Sha, Modelling beta transus temperature of titanium alloys using artificial neural network, Comput Mater. Sci, 32, 2005, 1-12.
  17. Topçu B., M. Sarıdemir, Prediction of properties of waste AAC aggregate concrete using artificial neural network, Comput Mater Sci, 41, 2007, 117-125.
  18. Altun F., Özgür Kişi, K. Aydin, Predicting the compressive strength of steel fiber added lightweight concrete using neural network, Comput Mater. Sci, 42, 2008, 259-265.
  19. Gajewski J., T. Sadowski, Sensitivity analysis of crack propagation in pavement bituminous layered structures using a hybrid system integrating artificial neural networks and finite element method, Comput Mater Sci, 82, 2014, 114-117.
  20. Häse F. , S. Valleau, E. Pyzer-Knapp, A. Aspuru-Guzik, Machine learning exciton dynamics, Chem Sci, 7 (8), 2016, 5139-5147.
  21. Wu H., A. Lorenson, B. Anderson, L. Witteman, H. T. Wu, B. Meredig et al., Robust FCC solute diffusion predictions from ab-initio machine learning methods, Comput Mater Sci, 134, 2017, 160-165.
  22. Fang S. F., M.P. Wang, W.H. Qi, F. Zheng, Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials, Comput Mater Sci, 44, 2008, 647-655.
  23. Liu X., W.C. Lu, C.R. Peng, Q. Su, J. Guo, Two semiempirical approaches for the prediction of oxide ionic conductivities in ABO3 perovskites, Comput Mater Sci, 46, 2009, 860-868.
  24. Ahmad A., A. Amjad, M. Denny, C. A. S. Bergström, Experimental and computational prediction of glass transition temperature of drugs, J Chem Inf Model, 54, 2014, 3396-3403.
  25. Javed S. G., A. Khan, A. Majid, A. M. Mirza, J. Bashir, Lattice constant prediction of orthorhombic ABO3perovskites using support vector machines, Comput Mater. Sci, 39, 2007, 627-634.
  26. Li C. H., Thing YH, Zeng YZ, Wang CM, Wu P. Prediction of lattice constant in perovskites of GdFeO3 structure. J Phys Chem Solids, 64, 2003, 2147-2156.
  27. Majid A., A. Khan, T. Choi, Predicting lattice constant of complex cubic perovskites using computational intelligence, Comput Mater Sci, 50, 2011, 1879-1888
  28. Majid A., A. Khan, G. Javed, A.M. Mirza, Lattice constant prediction of cubic and monoclinic perovskites using neural networks and support vector regression, Comput Mater Sci, 50, 2010, 363-372.
  29. Ward L., A. Agrawal, A. Choudhary, C.Wolverton, A general purpose machine learning framework for predicting properties of inorganic materials, npj Comput Mater, 2, 2016, 16028.
  30. Hastie T. R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed.; Springer: New York, 2009.
  31. Behler, J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys. 2011, 134, 074106.
  32. Li Z., J. R. Kermode, A. De Vita, Molecular Dynamics with On the-Fly Machine Learning of QuantumMechanical Forces. Phys. Rev. Lett. 2015, 114, 096405.
  33. Huan T. D., R. Batra, J. Chapman, S. Krishnan, L. Chen & R. Ramprasad, A Universal strategy for the creation of machine learning-based atomistic force fields, npj Computational Materials 3, 37, 2017.
  34. Botu V. R. Ramprasad, Learning scheme to predict atomic forces and accelerate materials simulations. Phys. Rev. B: Condens. Matter Mater. Phys. 92, 2015, 094306.
  35. Behler, J., M. Parrinello, Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 2007, 146401.
  36. Bartók A. P., G. Csányi, Gaussian approximation potentials: a brief tutorial introduction. Int. J. Quant. Chem. 115, 2015, 1051–1057.
  37. Friederich P., M. Konrad, T. Strunk, W. Wenzel, Machine learning of correlated dihederal potentials for atomistic molecular force fields, Scientific Reports 8, 2018, 2559.
  38. Mills K., M. Spanner, I. Tamblyn, Deep learning and the Schrodinger equation, Phys. Rev. A, 96, 042113
  39. Ryczko K., K. Mills, I. Luchak, C. Homenick, I. Tamblyn, Convolutional neural networks for atomistic systems, Comput Mater Sci, 149, 2018, 134-142.

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