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Merging Materials and Data Science: Opportunities, Challenges, and Education in Materials Informatics

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Abstract

Since the launch of the Materials Genome Initiative (MGI) the field of materials informatics (MI) emerged to remove the bottlenecks limiting the pathway towards rapid materials discovery. Although the machine learning (ML) and optimization techniques underlying MI were developed well over a decade ago, programs such as the MGI encouraged researchers to make the technical advancements that make these tools suitable for the unique challenges in materials science and engineering. Overall, MI has seen a remarkable rate in adoption over the past decade. However, for the continued growth of MI, the educational challenges associated with applying data science techniques to analyse materials science and engineering problems must be addressed. In this paper, we will discuss the growing use of materials informatics in academia and industry, highlight the need for educational advances in materials informatics, and discuss the implementation of a materials informatics course into the curriculum to jump-start interested students with the skills required to succeed in materials informatics projects.

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References

  1. H. Chan et al., “Machine learning coarse grained models for water,” Nat. Commun., 2019.

    Google Scholar 

  2. C.-T. Chen and G. X. Gu, “Composite Materials: Effect of Constituent Materials on Composite Performance: Exploring Design Strategies via Machine Learning (Adv. Theory Simul. 6/2019),” Adv. Theory Simulations, vol. 2. no. 6, 2019.

    Google Scholar 

  3. J. Behler, “Perspective: Machine learning potentials for atomistic simulations,” J. Chem. Phys., vol. 145. no. 17, 2016.

    Google Scholar 

  4. J. Hill, G. Mulholland, K. Persson, R. Seshadri, C. Wolverton, and B. Meredig, “Materials science with large-scale data and informatics: Unlocking new opportunities,” MRS Bull., vol. 41. no. 5, pp. 399–409, 2016.

    Article  Google Scholar 

  5. S. Curtarolo, G. L. W. Hart, M. B. Nardelli, N. Mingo, S. Sanvito, and O. Levy, “The high-throughput highway to computational materials design,” Nat. Mater., pp. 191–201, 2013.

    Google Scholar 

  6. Y. Liu, T. Zhao, W. Ju, S. Shi, S. Shi, and S. Shi, “Materials discovery and design using machine learning,” J. Mater., vol. 3. no. 3, pp. 159–177, 2017.

    Google Scholar 

  7. K. Takahashi and Y. Tanaka, “Material synthesis and design from first principle calculations and machine learning,” Comput. Mater. Sci., vol. 112, pp. 364–367, 2016.

    Article  CAS  Google Scholar 

  8. L. R. Zhao, K. Chen, Q. Yang, J. R. Rodgers, and S. H. Chiou, “Materials informatics for the design of novel coatings,” Surf. Coatings Technol., vol. 200. no. 5-6, pp. 1595–1599, 2005.

    Article  Google Scholar 

  9. S. Zeng, G. Li, Y. Zhao, R. Wang, and J. Ni, “Machine Learning-Aided Design of Materials with Target Elastic Properties,” J. Phys. Chem. C, vol. 123. no. 8, pp. 5042–5047, 2019.

    Article  Google Scholar 

  10. R. Liu, A. Kumar, Z. Chen, A. Agrawal, V. Sundararaghavan, and A. Choudhary, “A predictive machine learning approach for microstructure optimization and materials design,” Sci. Rep., vol. 10. no. 1, 2015.

    Google Scholar 

  11. S. Srinivasan et al., “Mapping Chemical Selection Pathways for Designing Multicomponent Alloys: An informatics framework for materials design,” Sci. Rep., 2015.

    Google Scholar 

  12. H. J. Kulik, “Making machine learning a useful tool in the accelerated discovery of transition metal complexes,” Wiley Interdiscip. Rev. Comput. Mol. Sci., 2019.

    Google Scholar 

  13. C. Kim, G. Pilania, and R. Ramprasad, “Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX3 Perovskites,” J. Phys. Chem. C, vol. 120. no. 27, pp. 14575–14580, 2016.

    Article  Google Scholar 

  14. H. Nakata and S. Bai, “Development of a new parameter optimization scheme for a reactive force field based on a machine learning approach,” J. Comput. Chem., vol. 40. no. 23, pp. 2000-2012, 2019.

    Google Scholar 

  15. P. Wang, Y. Shao, H. Wang, and W. Yang, “Accurate interatomic force field for molecular dynamics simulation by hybridizing classical and machine learning potentials,” Extrem. Mech. Lett., vol. 24, pp. 1–5, 2018.

    Article  Google Scholar 

  16. C. Chen, Z. Deng, R. Tran, H. Tang, I. H. Chu, and S. P. Ong, “Accurate force field for molybdenum by machine learning large materials data,” Phys. Rev. Mater., vol. 1. no. 4, 2017.

    Google Scholar 

  17. V. Botu and R. Ramprasad, “Learning scheme to predict atomic forces and accelerate materials simulations,” Phys. Rev. B - Condens. Matter Mater. Phys., vol. 92. no. 9, 2015.

    Google Scholar 

  18. M. A. Wood, M. A. Cusentino, B. D. Wirth, and A. P. Thompson, “Data-driven material models for atomistic simulation,” Phys. Rev. B, vol. 99. no. 18, 2019.

    Google Scholar 

  19. P. Bleiziffer, K. Schaller, and S. Riniker, “Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations,” J. Chem. Inf. Model., vol. 58. no. 3, pp. 579–590, 2018.

    Article  Google Scholar 

  20. S. Chmiela, H. E. Sauceda, K. R. Mmaiuller, and A. Tkatchenko, “Towards exact molecular dynamics simulations with machine-learned force fields,” Nat. Commun., 2018.

    Google Scholar 

  21. Y. Li et al., “Machine Learning Force Field Parameters from Ab Initio Data,” J. Chem. Theory Comput., vol. 13. no. 9, pp. 4492–4503, 2017.

    Article  Google Scholar 

  22. T. D. Huan, R. Batra, J. Chapman, S. Krishnan, L. Chen, and R. Ramprasad, “A universal strategy for the creation of machine learning-based atomistic force fields,” npj Comput. Mater., 2017.

    Google Scholar 

  23. P. Miles, L. Leon, R. C. Smith, and W. S. Oates, “Analysis of a multi-axial quantum informed ferroelectric continuum model: Part 1—uncertainty quantification,” J. Intell. Mater. Syst. Struct., vol. 29. no. 13, pp. 2823–2839, 2018.

    Article  Google Scholar 

  24. L. Leon, R. C. Smith, W. S. Oates, and P. Miles, “Analysis of a multi-axial quantum-informed ferroelectric continuum model: Part 2—sensitivity analysis,” J. Intell. Mater. Syst. Struct., vol. 29. no. 13, pp. 2840–2860, 2018.

    Article  Google Scholar 

  25. A. R. Paterson, B. J. Reich, R. C. Smith, A. G. Wilson, and J. L. Jones, “Bayesian approaches to uncertainty quantification and structure refinement from X-ray diffraction,” in Springer Series in Materials Science, 2018, pp. 81–102.

    Google Scholar 

  26. W. Xu and J. M. LeBeau, “A Convolutional Neural Network Approach to Thickness Determination using Position Averaged Convergent Beam Electron Diffraction,” Microsc. Microanal., vol. 23, 2017.

  27. Louis Columbus, “Roundup Of Machine Learning Forecasts And Market Estimates, 2018,” Forbes, 2018. [Online]. Available: https://www.forbes.com/sites/louiscolumbus/2018/02/18/roundup-of-machine-learning-forecasts-and-market-estimates-2018/#2c05d4602225. [Accessed: 10-Dec-2019].

    Google Scholar 

  28. “Citrine Informatics,” 2019. [Online]. Available: https://www.linkedin.com/company/citrine-informatics/insights/. [Accessed: 12-Dec-2019].

  29. Pattabiraman Kumaresh, “LinkedIn’s Most Promising Jobs of 2019,” 2019. [Online]. Available: https://blog.linkedin.com/2019/january/10/linkedins-most-promising-jobs-of-2019. [Accessed: 12-Dec-2019].

    Google Scholar 

  30. “Mathematicians and Statisticians,” Occupational Outlook Handbook, 2019. [Online]. Available: https://www.bls.gov/ooh/math/mathematicians-and-statisticians.htm. [Accessed: 12-Dec-2019].

  31. Linda Burtch, “The Burtch Works Study Salaries of Data Scientists & Predictive Analytics Professionals,” 2019.

    Google Scholar 

  32. V. Venkatraman and B. Alsberg, “Designing High-Refractive Index Polymers Using Materials Informatics,” Polymers (Basel)., 2018.

    Google Scholar 

  33. J. S. Peerless, N. J. B. Milliken, T. J. Oweida, M. D. Manning, and Y. G. Yingling, “Soft Matter Informatics: Current Progress and Challenges,” Adv. Theory Simulations, vol. 2. no. 1, 2019.

    Google Scholar 

  34. M. D. Manning, A. L. Kwansa, T. Oweida, J. S. Peerless, A. Singh, and Y. G. Yingling, “Progress in ligand design for monolayer-protected nanoparticles for nanobio interfaces,” Biointerphases, vol. 13. no. 6, 2018.

    Google Scholar 

  35. J. A. Nash, A. L. Kwansa, J. S. Peerless, H. S. Kim, and Y. G. Yingling, “Advances in molecular modeling of nanoparticle-nucleic acid interfaces,” Bioconjug. Chem., vol. 28. no. 1, pp. 3–10, 2017.

    Article  Google Scholar 

  36. N. K. Li et al., “Prediction of solvent-induced morphological changes of polyelectrolyte diblock copolymer micelles,” Soft Matter, vol. 11. no. 42, pp. 8236–8245, 2015.

    Article  Google Scholar 

  37. D. Weininger, “SMILES, a Chemical Language and Information System: 1: Introduction to Methodology and Encoding Rules,” J. Chem. Inf. Comput. Sci., vol. 28. no. 1, pp. 31–36, 1988.

    Article  Google Scholar 

  38. D. Weininger, A. Weininger, and J. L. Weininger, “SMILES. 2. Algorithm for Generation of Unique SMILES Notation,” J. Chem. Inf. Comput. Sci., vol. 29. no. 2, pp. 97–101, 1989.

    Article  Google Scholar 

  39. T. Lin-S. et al., “BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules,” ACS Cent. Sci., vol. 5. no. 9, pp. 1523–1531, 2019.

    Article  Google Scholar 

  40. De E. Guire et al., “Data-driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities,” J. Am. Ceram. Soc., vol. 102. no. 11, pp. 6385–6406, 2019.

    Article  Google Scholar 

  41. O. Kononova et al., “Text-mined dataset of inorganic materials synthesis recipes,” Sci. data, 2019.

    Google Scholar 

  42. H. M. Berman et al., “The Protein Data Bank (www.rcsb.org),” Nucleic Acids Res., 2000.

    Google Scholar 

  43. F. C. Bernstein et al., “The Protein Data Bank,” Eur. J. Biochem., vol. 80. no. 2, pp. 319–324, Nov. 1977.

    Article  Google Scholar 

  44. S. K. Burley et al., “RCSB Protein Data Bank: Biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy,” Nucleic Acids Res., vol. 47, pp. D464–D474, 2019.

    Article  CAS  Google Scholar 

  45. “Source: National Institute for Materials Science” [Online]. Available: https://www.nims.go.jp/eng/. [Accessed: 09-Dec-2019].

  46. P. Villars et al., “The Pauling File, Binaries Edition,” in Journal of Alloys and Compounds, 2004.

    Google Scholar 

  47. S. Otsuka, I. Kuwajima, J. Hosoya, Y. Xu, and M. Yamazaki, “PoLyInfo: Polymer database for polymeric materials design,” in Proceedings - 2011 International Conference on Emerging Intelligent Data and Web Technologies, EIDWT 2011, 2011.

    Google Scholar 

  48. K. Anderson et al., “Creating the Next Generation Materials Genome Initiative Workforce,” 2019.

    Google Scholar 

  49. R. Mansbach et al., “Reforming an undergraduate materials science curriculum with computational modules,” J Mater Educ, vol. 38, pp. 161–174, 2016.

    Google Scholar 

  50. “Data-Enabled Science and Engineering of Atomic Structure (SEAS)” [Online]. Available: https://www.mse.ncsu.edu/seas/traineeship/. [Accessed: 16-Dec-2019].

  51. W. Li, R. Jacobs, and D. Morgan, “Predicting the thermodynamic stability of perovskite oxides using machine learning models,” Comput. Mater. Sci., vol. 150, pp. 454–463, 2018.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695-7907, USA

    Thomas J. Oweida, Akhlak Mahmood, Matthew D. Manning, Sergei Rigin & Yaroslava G. Yingling

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  1. Thomas J. Oweida
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  2. Akhlak Mahmood
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  5. Yaroslava G. Yingling
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Oweida, T.J., Mahmood, A., Manning, M.D. et al. Merging Materials and Data Science: Opportunities, Challenges, and Education in Materials Informatics. MRS Advances 5, 329–346 (2020). https://doi.org/10.1557/adv.2020.171

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