Alloy synthesis and processing by semi-supervised text mining

Abstract

Abstract Alloy synthesis and processing determine the design of alloys with desired microstructure and properties. However, using data science to identify optimal synthesis-design routes from a specified set of starting materials has been limited by large-scale data acquisition. Text mining has made it possible to convert scientific text into structured data collections. Still, the complexity, diversity, and flexibility of synthesis and processing expressions, and the lack of annotated corpora with a gold standard severely hinder accurate and efficient extraction. Here we introduce a semi-supervised text mining method to extract the parameters corresponding to the sequence of actions of synthesis and processing. We automatically extract a total of 9853 superalloy synthesis and processing actions with chemical compositions from a corpus of 16,604 superalloy articles published up to 2022. These have then been used to capture an explicitly expressed synthesis factor for predicting γ′ phase coarsening. The synthesis factor derived from text mining significantly improves the performance of the data-driven γ′ size prediction model. The method thus complements the use of data-driven approaches in the search for relationships between synthesis and structures.