Optical device design is typically an iterative optimization process based on a good initial guess from prior reports. Optical properties databases are useful in this process but difficult to compile because their parsing requires finding relevant papers and manually converting graphical emissivity curves to data tables. Here, we present two contributions: one is a dataset of thermal emissivity records with design-related parameters, and the other is a software tool for automated colored curve data extraction from scientific plots. We manually collected 64 papers with 176 figures reporting thermal emissivity and automatically retrieved 153 colored curve data records. The automated figure analysis software pipeline uses Faster R-CNN for axes and legend object detection, EasyOCR for axes numbering recognition, and k-means clustering for colored curve retrieval. Additionally, we manually extracted geometry, materials, and method information from the text to add necessary metadata to each emissivity curve. Finally, we analyzed the dataset to determine the dominant classes of emissivity curves and determine the underlying design parameters leading to a type of emissivity profile.

The combination of three highly complementary scientific domains is demonstrated to rationalize bismuth ferrite (BiFeO3 [BFO]) crystallization pathways: text mining to extract processing recipes from existing literature, in situ X-ray scattering to follow crystallization pathways of solution-processed thin films, and ab initio calculations to develop a comprehensive understanding of thin-film formation from thermodynamic principles. Multiferroic BFO is chosen as an example material of interest for technological applications to demonstrate and validate this combined approach. Thermodynamic modeling showed that the production of a short-lived intermediate bismutite phase (Bi2O2CO3) influences the energies of competing reactions forming BFO and Bi2Fe4O9, increasing the thermodynamic driving force toward the formation of BFO rather than Bi2Fe4O9 impurity phase over a large temperature window. The synergy between the scientific domains is exemplified through the rational guidelines developed for controlling high-quality and phase-pure material fabrication.

We used data-driven methods to understand the formation of impurity phases in BiFeO₃ thin-film synthesis through the sol-gel technique. Using a high-quality dataset of 331 synthesis procedures and outcomes extracted manually from 177 scientific articles, we trained decision tree models that reinforce important experimental heuristics for the avoidance of phase impurities but ultimately show limited predictive capability. We find that several important synthesis features, identified by our model, are often not reported in the literature. To test our ability to correctly impute missing synthesis parameters, we attempted to reproduce nine syntheses from the literature with varying degrees of "missingness". We demonstrate how a text-mined dataset can be made useful by informing new controlled experiments and forming a better understanding for impurity phase formation in this complex oxide system.