Journal:An open experimental database for exploring inorganic materials

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Full article title An open experimental database for exploring inorganic materials
Journal Scientific Data
Author(s) Zakutayev, Andriy; Wunder, Nick; Schwarting, Marcus; Perkins, John D.;
White, Robert; Munch, Kristin; Tumas, William; Phillips, Caleb
Author affiliation(s) National Renewable Energy Laboratory
Primary contact Email: See original article
Year published 2018
Volume and issue 5
Page(s) 180053
DOI 10.1038/sdata.2018.53
ISSN 2052-4463
Distribution license Creative Commons Attribution 4.0 International
Website https://www.nature.com/articles/sdata201853
Download https://www.nature.com/articles/sdata201853.pdf (PDF)
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Abstract

The use of advanced machine learning algorithms in experimental materials science is limited by the lack of sufficiently large and diverse datasets amenable to data mining. If publicly open, such data resources would also enable materials research by scientists without access to expensive experimental equipment. Here, we report on our progress towards a publicly open High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). This database currently contains 140,000 sample entries, characterized by structural (100,000), synthetic (80,000), chemical (70,000), and optoelectronic (50,000) properties of inorganic thin film materials, grouped in >4,000 sample entries across >100 materials systems; more than a half of these data are publicly available. This article shows how the HTEM database may enable scientists to explore materials by browsing web-based user interface and an application programming interface. This paper also describes a HTE approach to generating materials data and discusses the laboratory information management system (LIMS) that underpins the HTEM database. Finally, this manuscript illustrates how advanced machine learning algorithms can be adopted to materials science problems using this open data resource.

Keywords: applied physics, electronic devices, materials chemistry, semiconductors, solar cells

Introduction

Machine learning is a branch of computer science concerned with algorithms that can develop models from the available data, reveal trends and correlation in this data, and make predictions about unavailable data. The predictions rely on data mining, the process of discovering patterns in large data sets using statistical methods. Machine learning methods have been recently successful in process automation, natural language processing, and computer vision, where large databases are available to support data-driven modeling efforts. These successes also sparked discussions about the potential of artificial intelligence in science[1] and the Fourth Paradigm[2] of data-driven scientific discovery. In materials science, applying artificial intelligence to data-driven materials discovery is important, because new materials often underpin major advances in modern technologies. For example, in advanced energy technologies, efficient solid state lighting was enabled by the use of gallium nitride in light-emitting diodes, electric cars were brought to life by intercalation materials used in lithium-ion batteries, and modern computers would not have been possible without the material silicon.

In computational materials science[3], machine learning methods have been recently used to predict structure[4], stability[5], and properties[6] of inorganic solid state materials. These results had been enabled by advances in simulation tools at multiple length scales.[7] The resulting simulated materials data are stored in ever-growing publicly-accessible computational property databases.[8][9][10] In contrast to computations, experimental materials discovery using machine learning is limited by the dearth of large and diverse datasets (Fig. 1). Large experimental datasets like the Inorganic Crystal Structure Database (ICSD)[11] contain hundreds of thousands of entries but are not diverse enough, as they contain only composition and structure of the materials. The diverse datasets like Landolt–Börnstein (http://materials.springer.com/)[12] or AtomWorks (http://crystdb.nims.go.jp/index_en.html)[13] contain hundreds to thousands of entries for different properties, so they are not large enough for training modern machine learning algorithms. Furthermore, none of these datasets contains synthesis information such as temperature or pressure, which is critical to making materials with target properties. Thus, machine learning for experimental materials research so far has focused on adoption of existing algorithms suitable for relatively small but complex datasets, such as collections of x-ray diffraction patterns[14], microscopy images[15], or materials microstructure.[16]

Fig1 Zakutayev SciData2018 5.jpg

Figure 1. Schematic scatter plot of the size vs. diversity of materials data in existing databases. The computational databases (red circles) are large and diverse. In contrast, experimental databases are either large or diverse, limiting application of machine learning algorithms.

One potential machine learning solution to create large and diverse materials datasets is natural language processing[17][18] from research articles published in scientific literature. However, the overwhelming majority of journal publications in experimental materials science is limited to what authors subjectively perceive as the most interesting of results, leading to large amounts of unpublished dark data.[19] Furthermore, the published papers are often biased towards positive research results[20], since the publication of failed experiments is discouraged in scientific literature. Such biased exclusion of negative results is a problem for machine learning, because many algorithms require both positive and negative results for efficient training. Finally, a very small fraction of these journal article publications are linked to the corresponding publications of underlying data, despite the increasing requirements from funding agencies[21] and encouragement from scientific journals[22] to make research data available to the public.

Here we describe our progress towards the High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). The HTEM DB is the first publicly available large collection of experimental data for inorganic materials synthesized using high-throughput experimental (HTE) thin film techniques. Currently, HTEM DB contains information about synthesis conditions, chemical composition, crystal structure, and optoelectronic property measurements of the materials. First, we provide an overview of the HTEM DB content, generated using the HTE experiments, and collected using materials data infrastructure. Next, we explain how to find, filter, and visualize the database content using its web interface on the example of one dataset. Finally, we illustrate how to perform machine learning on these data, using both supervised and unsupervised algorithms. More detailed technical information about the underlying experimental and data infrastructure of HTEM DB, as well as the machine learning algorithms, is provided in the Methods section of the paper.

Results

Database infrastructure and content

HTEM DB contains information about inorganic materials synthesized in thin film form, a combination that lends itself to high-throughput experimentation. The thin film sample libraries included in HTEM DB (semiconductors, metals, and insulators) are synthesized using combinatorial physical vapor deposition (PVD) methods, and each individual sample on the library is measured using spatially-resolved characterization techniques (Fig. 2). The selection of the materials to be synthesized is usually based on computational predictions or prior experimental literature, and on specific target applications (e.g., solar cell materials, transparent contacts, piezoelectrics, photoelectrochemical absorbers, etc.). Promising materials that result from the combinatorial synthesis and spatially resolved characterization are often optimized further using more traditional experimentation methods. A brief description of these high-throughput experimental techniques is provided in the Method section and in specialized review articles[23], with the focus on electronic and energy materials. Other classes of materials that are amenable to high-throughput experimentation, such as organic polymers[24] or homogeneous catalysts[25], are not discussed in this paper because they are currently not included in HTEM DB.


Fig2 Zakutayev SciData2018 5.jpg

Figure 2. Schematic illustration of high-throughput synthesis and characterization thin film approach, with composition and temperature gradients across the substrate. The materials for the HTE experiments are selected based on inputs from computations or literature, and the outputs of the HTE experimentation are the candidate materials for further optimization for specific applications.

As of 2018, there are 141,574 entries of thin film inorganic materials in HTEM DB. These entries are arranged in 4,356 sample libraries, distributed across approximately 100 unique materials systems. The content of HTEM DB is graphically summarized in Fig. 3 in a form of a bar-chart of the 28 most common metallic elements. The majority of these metallic elements appear in the form of compounds (oxides 45%, chalcogenides 30%, nitrides 20%), but a few also form intermetallics (5%). The reported data for these materials include synthesis conditions such as temperature (83,600), x-ray diffraction patterns (100,848), composition and thickness (72,952), optical absorption spectra (55,352), and electrical conductivities (32,912). Out of all the sample entries, only about 10% have been described in peer-reviewed literature, and the remaining 80–90% have not been published before. Currently, more than a half (50–60%) of all the measured properties are publicly available; the rest belongs to privately-funded projects and to ongoing or legacy public projects, for which data still needs to be curated.


Fig3 Zakutayev SciData2018 5.jpg

Figure 3. Metallic elements present in the HTEM DB, as determined from x-ray fluorescence measurement data and deposition precursor metadata. The majority of these elements are in form of oxide, nitride or chalcogenide compounds, with a few intermetallic compounds.


References

  1. ↑ "The AI revolution in science". Science. 7 July 2017. doi:10.1126/science.aan7064. http://www.sciencemag.org/news/2017/07/ai-revolution-science. 
  2. ↑ Hey, T.; Tansley, S.; Tolle, K. (2009). The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research. ISBN 9780982544204. https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discovery/. 
  3. ↑ Nosengo, N. (2016). "The Material Code: Machine-learning techniques could revolutionize how materials science is done". Nature 533: 22–25. doi:10.1038/533022a. 
  4. ↑ Meredig, B.; Agrawal, A.; Kirklin, S. et al. (2014). "Combinatorial screening for new materials in unconstrained composition space with machine learning". Physical Review B 89: 094104. doi:10.1103/PhysRevB.89.094104. 
  5. ↑ Hautier, G.; Fischer, C.C.; Jain, A. et al. (2010). "Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory". Chemistry of Materials 22 (12): 3762–3767. doi:10.1021/cm100795d. 
  6. ↑ Carrete, J.; Li, W.; Mingo, N. et al. (2014). "Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling". Physical Review X 4: 011019. doi:10.1103/PhysRevX.4.011019. 
  7. ↑ Rajan, K., ed. (2013). Informatics for Materials Science and Engineering (1st ed.). Butterworth-Heinemann. pp. 542. ISBN 9780123946140. 
  8. ↑ Jain, A.; Ong, S.P.; Hautier, G. et al. (2013). "Commentary: The Materials Project: A materials genome approach to accelerating materials innovation". APL Materials 1 (1): 011002. doi:10.1063/1.4812323. 
  9. ↑ Curtarolo, S.; Setyawan, W.; Wang, S. et al. (2012). "AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations". Computational Materials Science 58: 237–235. doi:10.1016/j.commatsci.2012.02.002. 
  10. ↑ Saal, J.E.; Kirklin, S.; Aykol, M. et al. (2013). "Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)". JOM 65 (11): 1501–1509. doi:10.1007/s11837-013-0755-4. 
  11. ↑ Belsky, A.; Hellenbrandt, M.; Karen, V.L.; Luksch, P. (2002). "New developments in the Inorganic Crystal Structure Database (ICSD): Accessibility in support of materials research and design". Acta Crystallographica B 58 (Pt 3 Pt 1): 364–9. PMID 12037357. 
  12. ↑ Hellwege, K.H. (1967). "Landolt-Börnstein, Numerical Data and Functional Relationships in Science and Technology". American Journal of Physics 35 (3): 291–292. doi:10.1119/1.1974060. 
  13. ↑ Xu, Y.; Yamazaki, M.; Villars, P. (2011). "Inorganic Materials Database for Exploring the Nature of Material". Japanese Journal of Applied Physics 50: 11S. doi:10.1143/JJAP.50.11RH02. 
  14. ↑ Mueller, T.; Kusne, A.G.; Ramprasad, R. (2016). "Chapter 4: Machine Learning in Materials Science". In Parrill, A.L.; Lipkowitz, K.B.. Reviews in Computational Chemistry. John Wiley & Sons, Inc. doi:10.1002/9781119148739.ch4. 
  15. ↑ Kalinin, S.V.; Sumpter, B.G.; Archibald, R.K. (2015). "Big–deep–smart data in imaging for guiding materials design". Nature Materials 14: 973–980. doi:10.1038/nmat4395. 
  16. ↑ Kalidindi, S.R.; De Graef, M. (2015). "Materials Data Science: Current Status and Future Outlook". Annual Review of Materials Research 45: 171-193. doi:10.1146/annurev-matsci-070214-020844. 
  17. ↑ Kajikawa, Y.; Abe, K.; Noda, S. (2006). "Filling the gap between researchers studying different materials and different methods: A proposal for structured keywords". Journal of Information Science 32 (6): 511-524. doi:10.1177/0165551506067125. 
  18. ↑ Kim, E.; Huang, K.; Tomala, A. et al. (2017). "Machine-learned and codified synthesis parameters of oxide materials". Scientific Data 4: 170127. doi:10.1038/sdata.2017.127. 
  19. ↑ Heidorn, P.B. (2008). "Shedding Light on the Dark Data in the Long Tail of Science". Library Trends 57 (2): 280-299. doi:10.1353/lib.0.0036. 
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  21. ↑ Gurin, J. (2014). Open Data Now: The Secret to Hot Startups, Smart Investing, Savvy Marketing, and Fast Innovation (1st ed.). McGraw-Hill Education. ISBN 9780071829779. 
  22. ↑ Vines, T.H.; Andrew, R.L.; Bock, D.G. et al. (2013). "Mandated data archiving greatly improves access to research data". The FASEB Journal 27 (4): 1304-1308. doi:10.1096/fj.12-218164. PMID 23288929. 
  23. ↑ Green, M.L.; Takeuchi, I.; Hattrci-Simpers, J.R. (2013). "Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials". Journal of Applied Physics 113 (23): 231101. doi:10.1063/1.4803530. 
  24. ↑ Meredith, J.C.; Karim, A.; Amis, E.J. (2002). "Combinatorial Methods for Investigations in Polymer Materials Science". MRS Bulletin 27 (4): 330-335. doi:10.1557/mrs2002.101. 
  25. ↑ Snively, C.M.; Oskarsdottir, G.; Lauterbach, J. (2001). "Chemically sensitive parallel analysis of combinatorial catalyst libraries". Catalysis Today 67 (4): 357-368. doi:10.1016/S0920-5861(01)00328-5. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.

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