Hostname: page-component-6766d58669-wvcvf Total loading time: 0 Render date: 2026-05-22T14:01:25.078Z Has data issue: false hasContentIssue false
  • English
  • Français

An informatics software stack for point defect-derived opto-electronic properties: the Asphalt Project

Published online by Cambridge University Press:  02 September 2019

Jonathon N. Baker ,
Preston C. Bowes ,
Joshua S. Harris  and
Douglas L. Irving
Jonathon N. Baker
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Suite 3002, Raleigh, NC 27695, USA
Preston C. Bowes
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Suite 3002, Raleigh, NC 27695, USA
Joshua S. Harris
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Suite 3002, Raleigh, NC 27695, USA
Douglas L. Irving*
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Suite 3002, Raleigh, NC 27695, USA
*
Address all correspondence to Douglas L. Irving at dlirving@ncsu.edu
Get access

Abstract

Computational acceleration of performance metric-based materials discovery via high-throughput screening and machine learning methods is becoming widespread. Nevertheless, development and optimization of the opto-electronic properties that depend on dilute concentrations of point defects in new materials have not significantly benefited from these advances. Here, the authors present an informatics and simulation suite to computationally accelerate these processes. This will enable faster and more fundamental materials research, and reduce the cost and time associated with the materials development cycle. Analogous to the new avenues enabled by current first-principles-based property databases, this type of framework will open entire new research frontiers as it proliferates.

Information

Type
Artificial Intelligence Prospectives
Information
MRS Communications , Volume 9 , Issue 3 , September 2019 , pp. 839 - 845
Copyright
Copyright © Materials Research Society 2019 

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.)

Article purchase

Temporarily unavailable

References

1.Holdren, J.P., Kalil, T., and Wadia, C.: Materials Genome Initiative for Global Competitiveness (National Science and Technology Council OSTP, Washington, USA, 2011).Google Scholar
2.Jain, A., Ong, S.P., Hautier, G., Chen, W., Richards, W.D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., and Persson, K.A.: Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).10.1063/1.4812323Google Scholar
3.Saal, J.E., Kirklin, S., Aykol, M., Meredig, B., and Wolverton, C.: Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD). JOM 65, 1501 (2013).Google Scholar
4.Curtarolo, S., Setyawan, W., Hart, G.L., Jahnatek, M., Chepulskii, R.V., Taylor, R.H., Wang, S., Xue, J., Yang, K., Levy, O., Mehl, M.J., Stokes, H.T., Demchenko, D.O., and Morgan, D.: AFLOW: an automatic framework for high-throughput materials discovery. Comput. Mater. Sci. 58, 218 (2012).Google Scholar
5.Ye, W., Chen, C., Dwaraknath, S., Jain, A., Ong, S.P., and Persson, K.A.: Harnessing the Materials Project for machine-learning and accelerated discovery. MRS Bull. 43, 664 (2018).Google Scholar
6.Toher, C., Oses, C., Plata, J.J., Hicks, D., Rose, F., Levy, O., de Jong, M., Asta, M., Fornari, M., Nardelli, M.B., and Curtarolo, S.: Combining the AFLOW GIBBS and elastic libraries to efficiently and robustly screen thermomechanical properties of solids. Phys. Rev. Mater. 1, 015401 (2017).Google Scholar
7.Jain, A., Shin, Y., and Persson, K.A.: Computational predictions of energy materials using density functional theory. Nat. Rev. Mater 1, 15004 (2016).Google Scholar
8.Alberi, K., Nardelli, M.B., Zakutayev, A., Mitas, L., Curtarolo, S., Jain, A., Fornari, M., Marzari, N., Takeuchi, I., Green, M.L., Kanatzidis, M., Toney, M.F., Butenko, S., Meredig, B., Lany, S., Kattner, U., Davydov, A., Toberer, E.S., Stevanovic, V., Walsh, A., Park, N.-G., Aspuru-Guzik, A., Tabor, D.P., Nelson, J., Murphy, J., Setlur, A., Gregoire, J., Li, H., Xiao, R., Ludwig, A., Martin, L.W., Rappe, A.M., Wei, S.-H., and Perkins, J.: The 2019 materials by design roadmap. J. Phys. D: Appl. Phys. 52, 013001 (2019).10.1088/1361-6463/aad926Google Scholar
9.Broberg, D., Medasani, B., Zimmermann, N.E., Yu, G., Canning, A., Haranczyk, M., Asta, M., and Hautier, G.: PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators. Comput. Phys. Commun. 226, 165 (2018).10.1016/j.cpc.2018.01.004Google Scholar
10.Baker, J.N., Bowes, P.C., Long, D.M., Moballegh, A., Harris, J.S., Dickey, E.C., and Irving, D.L.: Defect mechanisms of coloration in Fe-doped SrTiO3 from first principles. Appl. Phys. Lett. 110, 122903 (2017).10.1063/1.4978861Google Scholar
11.Bowes, P.C., Baker, J.N., Harris, J.S., Behrhorst, B.D., and Irving, D.L.: Influence of impurities on the high temperature conductivity of SrTiO3. Appl. Phys. Lett. 112, 022902 (2018).10.1063/1.5000363Google Scholar
12.Baker, J.N., Bowes, P.C., Harris, J.S., and Irving, D.L.: Mechanisms governing metal vacancy formation in BaTiO3 and SrTiO3. J. Appl. Phys. 124, 114101 (2018).Google Scholar
13.Harris, J.S., Baker, J.N., Gaddy, B.E., Bryan, I., Bryan, Z., Mirrieless, K.J., Collazo, R., Sitar, Z., and Irving, D.L.: On compensation in Si-doped AlN. Appl. Phys. Lett. 112, 152101 (2018).Google Scholar
14.Baker, J.N., Bowes, P.C., and Irving, D.L.: Hydrogen solubility in donor-doped SrTiO3 from first principles. Appl. Phys. Lett. 113, 132904 (2018).10.1063/1.5047793Google Scholar
15.Heyd, J., Scuseria, G.E., and Ernzerhof, M.: Hybrid functionals based on a screened Coulomb potential. J. Chem. Phys. 118, 8207 (2003).Google Scholar
16.Perdew, J.P., Burke, K., and Ernzerhof, M.: Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 77, 3865 (1996).Google Scholar
17.Freysoldt, C., Grabowski, B., Hickel, T., Neugebauer, J., Kresse, G., Janotti, A., and Van De Walle, C.G.: First-principles calculations for point defects in solids. Rev. Mod. Phys. 86, 253 (2014).Google Scholar
18.Chevrier, V.L., Ong, S.P., Armiento, R., Chan, M.K., and Ceder, G.: Hybrid density functional calculations of redox potentials and formation energies of transition metal compounds. Phys. Rev. B 82, 075122 (2010).10.1103/PhysRevB.82.075122Google Scholar
19.Kresse, G. and Hafner, J.: Ab initio molecular dynamics for liquid metals. Phys. Rev. B Condens. Matter Mater. Phys. 47, 558 (1993).10.1103/PhysRevB.47.558Google Scholar
20.Van de Walle, C.G., Laks, D., Neumark, G., and Pantelides, S.: First-principles calculations of solubilities and doping limits: Li, Na, and N in ZnSe. Phys. Rev. B 47, 9425 (1993).10.1103/PhysRevB.47.9425Google Scholar
21.Mueller, K., Von Waldkirch, T., Berlinger, W., and Faughnan, B.: Photochromic Fe5+ (3d3) in SrTiO3 evidence from paramagnetic resonance. Solid State Commun. 9, 1097 (1971).Google Scholar
22.Baiatu, T., Waser, R., and Haerdtl, K.-H.: dc Electrical Degradation of Perovskite-Type Titanates: III, A Model of the Mechanism. J. Am. Ceram. Soc. 73, 1663 (1990).10.1111/j.1151-2916.1990.tb09811.xGoogle Scholar
23.Chan, N.-H., Sharma, R., and Smyth, D.M.: Nonstoichiometry in SrTiO3. J. Electrochem. Soc. 128, 1762 (1981).Google Scholar
24.Mehnke, F., Wernicke, T., Pingel, H., Kuhn, C., Reich, C., Kueller, V., Knauer, A., Lapeyrade, M., Weyers, M., and Kneissl, M.: Highly conductive n-AlxGa1-xN layers with aluminum mole fractions above 80%. Appl. Phys. Lett. 103, 212109 (2013).Google Scholar
25.Taniyasu, Y., Kasu, M., and Kobayashi, N.: Intentional control of n-type conduction for Si-doped AlN and AlxGa1-xN (.42≤x<1). Appl. Phys. Lett. 81, 1255 (2002).10.1063/1.1499738Google Scholar
26.Uedono, A., Ishibashi, S., Keller, S., Moe, C., Cantu, P., Katona, T., Kamber, D., Wu, Y., Letts, E., Newman, S., Nakamura, S., Speck, J.S., Mishra, U.K., DenBaars, S.P., Onuma, T., and Chichibu, S.F.: Vacancy-oxygen complexes and their optical properties in AlN epitaxial films studied by positron annihilation. J. Appl. Phys. 105, 054501 (2009).Google Scholar
27.Bryan, I., Bryan, Z., Washiyama, S., Reddy, P., Gaddy, B.E., Sarkar, B., Breckenridge, M.H., Guo, Q., Graziano, M.B., Tweedie, J., Mita, S., Irving, D.L., Collazo, R., and Sitar, Z.: Doping and compensation in Al-rich AlGaN grown on single crystal AlN and sapphire by MOCVD. Appl. Phys. Lett. 112, 062102 (2018).Google Scholar