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Photovoltaics Informatics: Harnessing Energy Science via Data-driven Approaches

Published online by Cambridge University Press:  12 January 2012

Changwon Suh ,
Kristin Munch ,
David Biagioni ,
Stephen Glynn ,
John Scharf ,
Miguel A. Contreras ,
John D. Perkins ,
Brent P. Nelson  and
Wesley B. Jones
Changwon Suh
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, USA
Kristin Munch
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, USA
David Biagioni
Affiliation:
Department of Applied Mathematics, University of Colorado, Boulder, CO 80309, USA
Stephen Glynn
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, USA
John Scharf
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, USA
Miguel A. Contreras
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, USA
John D. Perkins
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, USA
Brent P. Nelson
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, USA
Wesley B. Jones
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, USA
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Abstract

We discuss our current research focus on photovoltaic (PV) informatics, which is dedicated to functionality enhancement of solar materials through data management and data mining-aided, integrated computational materials engineering (ICME) for rapid screening and identification of multi-scale processing/structure/property/performance relationships. Our current PV informatics research ranges from transparent conducting oxides (TCO) to solar absorber materials. As a test bed, we report on examples of our current data management system for PV research and advanced data mining to improve the performance of solar cells such as CuInxGa1-xSe2 (CIGS) aiming at low-cost and high-rate processes. For the PV data management, we show recent developments of a strategy for data modeling, collection and aggregation methods, and construction of data interfaces, which enable proper archiving and data handling for data mining. For scientific data mining, the value of high-dimensional visualizations and non-linear dimensionality reduction is demonstrated to quantitatively assess how process conditions or properties are interconnected in the context of the development of Al-doped ZnO (AZO) thin films as the TCO layers for CIGS devices. Such relationships between processing and property of TCOs lead to optimal process design toward enhanced performance of CIGS cells/devices.

Keywords

photovoltaiccombinatorial synthesistransparent conductor
Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 1425: Symposium UU – Combinatorial and High-Throughput Methods in Materials Science , 2012 , mrsf11-1425-uu06-04
Copyright
Copyright © Materials Research Society 2012

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References

REFERENCES

1. Crowley, J., “Thoughts on Digital Data, SIAM News,” December 14, 2010.Google Scholar
2. Simmons, J. and Zhao, J.-C., JOM, 63, 24 (2011).Google Scholar
3. Suh, C., Gorrie, C.W., Perkins, J.D., Graf, P.A., and Jones, W.B., PVSC. Proc., Honolulu, HI, 002497 (2010).Google Scholar
4. Chong, I., Albin, S.L., and Jun, C., IIE Trans. Qual. Rel. Eng., 39, 795 (2007).Google Scholar
5. Suh, C., Gorrie, C.W., Perkins, J.D., Graf, P.A., and Jones, W.B., Acta Mater. 59, 630 (2011).Google Scholar
6. Dengiz, O., Smith, A.E. and Nettleship, I., Int. J. Prod. Res. 44, 2839 (2006).Google Scholar
7. Scott, D.J. et al. ., J. Chem. Inf. Model. 48, 449 (2008).Google Scholar
8. Ortiz, C., Eriksson, O., and Klintenberg, M., Comp. Mater. Sci. 44, 1042 (2009).Google Scholar
9. Stan, M., Mater. Today, 12, 20 (2009).Google Scholar
10. Baumann, S., “Outsourcing of Analysis: Choosing the Right Technique,” Microelectronic Failure Analysis: Desk Reference 2002 Supplement, ed. Electronic Device Failure Society Publications Committee (ASM International, Materials Park, OH, 2002), pp. 1–12.Google Scholar
11. Foster, I., Vöckler, J., Wilde, M., and Zhao, Y., Proc. 2003 CIDR Conf. Innov. Data Sys. Res. (2003).Google Scholar
12. Wemple, S.H., and DiDomenico, M., Phys. Rev. Lett. 23, 1156 (1969).Google Scholar
13. Suh, C., Glynn, S., Biagioni, D., Scharf, J., Contreras, M.A., Noufi, R., and Jones, W.B., PVSC. Proc., Seattle, WA, NREL/CP-2C00–50693 (2011).Google Scholar
14. Scheer, R. and Schock, H., Chalcogenide Photovoltaics: Physics, Technologies, and Thin Film Devices (Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, 2011) p. 305.Google Scholar
15. Chen, P. and Pin-Shan, P., ACM Trans. Database Sys. 1, 9 (1976).Google Scholar
16. Connolly, T. and Begg, C., Database Solutions: A Step-by-step Guide to Building Databases, 2 nd ed. (Addison Wesely, Essex, 2004), p. 413.Google Scholar
17. Suh, C., Kim, K., Berry, J.J., Lee, J., and Jones, W.B., in Data Mining-Aided Crystal Engineering for the Design of Transparent Conducting Oxides, (Mater. Res. Soc. Proc., 1315, 2011) mrsf10-1315-mm02-07.Google Scholar
18. Leban, G., Zupan, B., Vidmar, G. and Bratko, I., Data Min. Knowl. Disc. 13, 119 (2006).Google Scholar