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

Abstract

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 communitywide 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, 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.

Materials‐Informatics‐Assisted High‐Yield Synthesis of 2D Nanomaterials through Exfoliation

“…In recent materials science, data science has been applied for an efficient discovery of compounds and design of functions. [48][49][50][51][52][53][54][55][56][57][58][59][60][61] The data science accelerates the exploration of functional materials, such as energy-related materials, in a minimum number of experiments. [37][38][39][40][41][42][43][44] However, the informatics approach has not been fully applied to synthesis processes and structure control of nanomaterials in experimental studies.…”

Materials Informatics Screening of Li‐Rich Layered Oxide Cathode Materials with Enhanced Characteristics Using Synthesis Data

“…Albeit this assumption can be seen as unreasonable it allowed us to significantly enhance the results of modeling. The descriptors describing the synthesis process are efficiently used in virtual screening applications in materials science . Heat‐treatment information including the temperature and time required for the calcination and sintering processes has been normalized to a scale of zero to one accepting the maximal and minimal known temperatures and time limits.…”

Opportunities and challenges of text mining in materials research

“… Young et al. (2018) developed a semi-automated TM pipeline to extract and analyze the growth conditions for four different oxide materials synthesized with pulsed laser deposition technique.…”

“…Diverse approaches to collaborative data management have been widely used in projects such as OpenEI ( www.openei.org ), Folding@home ( www.foldingathome.org ) and others ( Zhai et al., 2013 ; Doan et al., 2011 ), as well as have proven to be highly efficient for gathering a large amount of data. To date, only a few projects have utilized crowd-sourcing in materials science TM research ( Young et al., 2018 ; Tchoua et al., 2016 ). But development of a collaborative data collection platform for application of NLP in materials science meets several challenges.…”