Data Mining for better material synthesis: the case of pulsed laser deposition of complex oxides

• Published in: arXiv.org
• Main Authors: Young, Steven R, Maksov, Artem, Ziatdinov, Maxim, Cao, Ye, Burch, Matthew, Janakiraman Balachandran, Li, Linglong, Suhas Somnath, Patton, Robert M, Kalinin, Sergei V, Vasudevan, Rama K
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 01.03.2018
• Subjects: Data mining; First principles; Functional materials; Machine learning; Mathematical analysis; Optimization; Outliers (statistics); Oxide coatings; Physics - Materials Science; Pulsed laser deposition; Pulsed lasers; Software development tools; Synthesis
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1710.07721

The pursuit of more advanced electronics, finding solutions to energy needs, and tackling a wealth of social issues often hinges upon the discovery and optimization of new functional materials that enable disruptive technologies or applications. 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, and almost all of it is not collated and thus cannot be used in its entirety. Many of these limitations can be circumvented if the experimentalist has access to systematized collections of prior experimental procedures and results that can be analyzed and built upon. 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 and codes available on https://github.com/ORNL-DataMatls). The results allow visualization of growth windows, trends and outliers, and which can serve as a template for analyzing the distribution of growth conditions, provide starting points for related compounds and act as feedback for first-principles calculations. Such tools will comprise an integral part of the materials design schema in the coming decade.