Hostname: page-component-848d4c4894-xfwgj Total loading time: 0 Render date: 2024-07-02T12:24:05.836Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  10 March 2020

Thomas J. Oweida ,
Akhlak Mahmood ,
Matthew D. Manning ,
Sergei Rigin  and
Yaroslava G. Yingling Open the ORCID record for Yaroslava G. Yingling [Opens in a new window]
Thomas J. Oweida
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC 27695-7907, USA
Akhlak Mahmood
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC 27695-7907, USA
Matthew D. Manning
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC 27695-7907, USA
Sergei Rigin
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC 27695-7907, USA
Yaroslava G. Yingling*
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC 27695-7907, USA
*
*(Email: yara_yingling@ncsu.edu)
Get access

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.

Keywords

educationinformaticsmaterial availabilitydata
Type
Articles
Information
MRS Advances , Volume 5 , Issue 7: BROADER IMPACT , 2020 , pp. 329 - 346
Copyright
Copyright © Materials Research Society 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References:

Chan, H.et al., “Machine learning coarse grained models for water,” Nat. Commun., 2019.Google Scholar
Chen, C.-T. and Gu, G. X., “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.CrossRefGoogle Scholar
Behler, J., “Perspective: Machine learning potentials for atomistic simulations,” J. Chem. Phys., vol. 145, no. 17, 2016.Google ScholarPubMed
Hill, J., Mulholland, G., Persson, K., Seshadri, R., Wolverton, C., and Meredig, B., “Materials science with large-scale data and informatics: Unlocking new opportunities,” MRS Bull., vol. 41, no. 5, pp. 399–409, 2016.CrossRefGoogle Scholar
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., and Levy, O., “The high-throughput highway to computational materials design,” Nat. Mater., pp. 191–201, 2013.CrossRefGoogle ScholarPubMed
Liu, Y., Zhao, T., Ju, W., Shi, S., Shi, S., and Shi, S., “Materials discovery and design using machine learning,” J. Mater., vol. 3, no. 3, pp. 159–177, 2017.Google Scholar
Takahashi, K. and Tanaka, Y., “Material synthesis and design from first principle calculations and machine learning,” Comput. Mater. Sci., vol. 112, pp. 364–367, 2016.CrossRefGoogle Scholar
Zhao, L. R., Chen, K., Yang, Q., Rodgers, J. R., and Chiou, S. H., “Materials informatics for the design of novel coatings,” Surf. Coatings Technol., vol. 200, no. 5–6, pp. 1595–1599, 2005.CrossRefGoogle Scholar
Zeng, S., Li, G., Zhao, Y., Wang, R., and Ni, J., “Machine Learning-Aided Design of Materials with Target Elastic Properties,” J. Phys. Chem. C, vol. 123, no. 8, pp. 5042–5047, 2019.CrossRefGoogle Scholar
Liu, R., Kumar, A., Chen, Z., Agrawal, A., Sundararaghavan, V., and Choudhary, A., “A predictive machine learning approach for microstructure optimization and materials design,” Sci. Rep., vol. 10, no. 1, 2015.Google Scholar
Srinivasan, S.et al., “Mapping Chemical Selection Pathways for Designing Multicomponent Alloys: An informatics framework for materials design,” Sci. Rep., 2015.CrossRefGoogle ScholarPubMed
Kulik, H. J., “Making machine learning a useful tool in the accelerated discovery of transition metal complexes,” Wiley Interdiscip. Rev. Comput. Mol. Sci., 2019.Google Scholar
Kim, C., Pilania, G., and Ramprasad, R., “Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX3 Perovskites,” J. Phys. Chem. C, vol. 120, no. 27, pp. 14575–14580, 2016.CrossRefGoogle Scholar
Nakata, H. and Bai, S., “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
Wang, P., Shao, Y., Wang, H., and Yang, W., “Accurate interatomic force field for molecular dynamics simulation by hybridizing classical and machine learning potentials,” Extrem. Mech. Lett., vol. 24, pp. 1–5, 2018.CrossRefGoogle Scholar
Chen, C., Deng, Z., Tran, R., Tang, H., Chu, I. H., and Ong, S. P., “Accurate force field for molybdenum by machine learning large materials data,” Phys. Rev. Mater., vol. 1, no. 4, 2017.Google Scholar
Botu, V. and Ramprasad, R., “Learning scheme to predict atomic forces and accelerate materials simulations,” Phys. Rev. B - Condens. Matter Mater. Phys., vol. 92, no. 9, 2015.CrossRefGoogle Scholar
Wood, M. A., Cusentino, M. A., Wirth, B. D., and Thompson, A. P., “Data-driven material models for atomistic simulation,” Phys. Rev. B, vol. 99, no. 18, 2019.CrossRefGoogle Scholar
Bleiziffer, P., Schaller, K., and Riniker, S., “Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations,” J. Chem. Inf. Model., vol. 58, no. 3, pp. 579–590, 2018.CrossRefGoogle ScholarPubMed
Chmiela, S., Sauceda, H. E., Müller, K. R., and Tkatchenko, A., “Towards exact molecular dynamics simulations with machine-learned force fields,” Nat. Commun., 2018.CrossRefGoogle ScholarPubMed
Li, Y.et al., “Machine Learning Force Field Parameters from Ab Initio Data,” J. Chem. Theory Comput., vol. 13, no. 9, pp. 4492–4503, 2017.CrossRefGoogle ScholarPubMed
Huan, T. D., Batra, R., Chapman, J., Krishnan, S., Chen, L., and Ramprasad, R., “A universal strategy for the creation of machine learning-based atomistic force fields,” npj Comput. Mater., 2017.CrossRefGoogle Scholar
Miles, P., Leon, L., Smith, R. C., and Oates, W. S., “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.CrossRefGoogle Scholar
Leon, L., Smith, R. C., Oates, W. S., and Miles, P., “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.CrossRefGoogle Scholar
Paterson, A. R., Reich, B. J., Smith, R. C., Wilson, A. G., and Jones, J. L., “Bayesian approaches to uncertainty quantification and structure refinement from X-ray diffraction,” in Springer Series in Materials Science, 2018, pp. 81–102.Google Scholar
Xu, W. and LeBeau, J. M., “A Convolutional Neural Network Approach to Thickness Determination using Position Averaged Convergent Beam Electron Diffraction,” Microsc. Microanal., vol. 23, 2017.CrossRefGoogle Scholar
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
Citrine Informatics,” 2019. [Online]. Available: https://www.linkedin.com/company/citrine-informatics/insights/. [Accessed: 12-Dec-2019].Google Scholar
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
Mathematicians and Statisticians,” Occupational Outlook Handbook, 2019. [Online]. Available: https://www.bls.gov/ooh/math/mathematicians-and-statisticians.htm. [Accessed: 12-Dec-2019].Google Scholar
Linda Burtch, “The Burtch Works Study Salaries of Data Scientists & Predictive Analytics Professionals,” 2019.Google Scholar
Venkatraman, V. and Alsberg, B., “Designing High-Refractive Index Polymers Using Materials Informatics,” Polymers (Basel)., 2018.Google ScholarPubMed
Peerless, J. S., Milliken, N. J. B., Oweida, T. J., Manning, M. D., and Yingling, Y. G., “Soft Matter Informatics: Current Progress and Challenges,” Adv. Theory Simulations, vol. 2, no. 1, 2019.CrossRefGoogle Scholar
Manning, M. D., Kwansa, A. L., Oweida, T., Peerless, J. S., Singh, A., and Yingling, Y. G., “Progress in ligand design for monolayer-protected nanoparticles for nanobio interfaces,” Biointerphases, vol. 13, no. 6, 2018.CrossRefGoogle ScholarPubMed
Nash, J. A., Kwansa, A. L., Peerless, J. S., Kim, H. S., and Yingling, Y. G., “Advances in molecular modeling of nanoparticle-nucleic acid interfaces,” Bioconjug. Chem., vol. 28, no. 1, pp. 3–10, 2017.CrossRefGoogle ScholarPubMed
Li, N. K.et al., “Prediction of solvent-induced morphological changes of polyelectrolyte diblock copolymer micelles,” Soft Matter, vol. 11, no. 42, pp. 8236–8245, 2015.CrossRefGoogle ScholarPubMed
Weininger, D., “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.CrossRefGoogle Scholar
Weininger, D., Weininger, A., and Weininger, J. L., “SMILES. 2. Algorithm for Generation of Unique SMILES Notation,” J. Chem. Inf. Comput. Sci., vol. 29, no. 2, pp. 97–101, 1989.CrossRefGoogle Scholar
Lin, T.-S.et al., “BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules,” ACS Cent. Sci., vol. 5, no. 9, pp. 1523–1531, 2019.CrossRefGoogle ScholarPubMed
De Guire, E.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.CrossRefGoogle Scholar
Kononova, O.et al., “Text-mined dataset of inorganic materials synthesis recipes,” Sci. data, 2019.Google ScholarPubMed
Berman, H. M.et al., “The Protein Data Bank (www.rcsb.org),” Nucleic Acids Res., 2000.CrossRefGoogle ScholarPubMed
Bernstein, F. C.et al., “The Protein Data Bank,” Eur. J. Biochem., vol. 80, no. 2, pp. 319–324, Nov. 1977.CrossRefGoogle ScholarPubMed
Burley, S. K.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.CrossRefGoogle Scholar
“Source: National Institute for Materials Science.” [Online]. Available: https://www.nims.go.jp/eng/. [Accessed: 09-Dec-2019].Google Scholar
Villars, P.et al., “The Pauling File, Binaries Edition,” in Journal of Alloys and Compounds, 2004.CrossRefGoogle Scholar
Otsuka, S., Kuwajima, I., Hosoya, J., Xu, Y., and Yamazaki, M., “PoLyInfo: Polymer database for polymeric materials design,” in Proceedings - 2011 International Conference on Emerging Intelligent Data and Web Technologies, EIDWT 2011, 2011.CrossRefGoogle Scholar
Anderson, K.et al., “Creating the Next Generation Materials Genome Initiative Workforce,” 2019.Google Scholar
Mansbach, R.et al., “Reforming an undergraduate materials science curriculum with computational modules,” J Mater Educ, vol. 38, pp. 161–174, 2016.Google Scholar
“Data-Enabled Science and Engineering of Atomic Structure (SEAS).” [Online]. Available: https://www.mse.ncsu.edu/seas/traineeship/. [Accessed: 16-Dec-2019].Google Scholar
Li, W., Jacobs, R., and Morgan, D., “Predicting the thermodynamic stability of perovskite oxides using machine learning models,” Comput. Mater. Sci., vol. 150, pp. 454–463, 2018.CrossRefGoogle Scholar