Data mining for better material synthesis: The case of pulsed laser deposition of complex oxides

Young, Steven R.; Maksov, Artem; Ziatdinov, Maxim; Cao, Ye; Burch, Matthew; Balachandran, Janakiraman; Li, Linglong; Somnath, Suhas; Patton, Robert M.; Kalinin, Sergei V.; Vasudevan, Rama K.

doi:10.1063/1.5009942

Title: Data mining for better material synthesis: The case of pulsed laser deposition of complex oxides

Journal Article · Tue Mar 20 00:00:00 EDT 2018 · Journal of Applied Physics

DOI:https://doi.org/10.1063/1.5009942· OSTI ID:1468242

^[1]; Maksov, Artem ^[2]; Ziatdinov, Maxim ^[1]; Cao, Ye ^[1]; Burch, Matthew ^[1]; Balachandran, Janakiraman ^[3]; Li, Linglong ^[4]; Somnath, Suhas ^[1]; Patton, Robert M. ^[1];

^[1];

^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Shell Technology Centre, Bangalore (India)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Xi'an Jiaotong Univ., Shaanxi (China)

The pursuit of more advanced electronics, and finding solutions to energy needs often hinges upon the discovery and optimization of new functional materials. However, the discovery rate of these materials is alarmingly low. Much of the information that could drive this rate higher is scattered across tens of thousands of papers in the extant literature published over several decades but is not in an indexed form, and cannot be used in entirety without substantial effort. Many of these limitations can be circumvented if the experimentalist has access to systematized collections of prior experimental procedures and results. Here, we investigate the property-processing relationship during growth of oxide films by pulsed laser deposition. To do so, we develop an enabling software tool to (1) mine the literature of relevant papers for synthesis parameters and functional properties of previously studied materials, (2) enhance the accuracy of this mining through crowd sourcing approaches, (3) create a searchable repository that will be a community-wide resource enabling material scientists to leverage this information, and (4) provide through the Jupyter notebook platform, simple machine-learning-based analysis to learn the complex interactions between growth parameters and functional properties (all data/codes available on https://github.com/ORNL-DataMatls). The results allow visualization of growth windows, trends and outliers, which can serve as a template for analyzing the distribution of growth conditions, provide starting points for related compounds and act as a feedback for first-principles calculations. Such tools will comprise an integral part of the materials design schema in the coming decade.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1468242

Alternate ID(s):: OSTI ID: 1427325

Journal Information:: Journal of Applied Physics, Vol. 123, Issue 11; ISSN 0021-8979

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 23 works

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