Automated pipeline for superalloy data by text mining

• Published in: npj computational materials 2022-01, Vol.8 (1), p.1-12, Article 9
• Main Authors: Wang, Weiren, Jiang, Xue, Tian, Shaohan, Liu, Pei, Dang, Depeng, Su, Yanjing, Lookman, Turab, Xie, Jianxin
• Format: Article
• Language: English
• Subjects: 639/301/1023/1026; 639/301/1034/1037; Algorithms; Alloys; Artificial intelligence; Automation; Characterization and Evaluation of Materials; Chemical composition; Chemistry and Materials Science; Cobalt base alloys; Computational Intelligence; Data mining; Dictionaries; Genomes; Laboratories; Language; Learning algorithms; Liquidus; Machine learning; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Natural language processing; Solidus; Superalloys; Text categorization; Theoretical
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-021-00687-2

Data provides a foundation for machine learning, which has accelerated data-driven materials design. The scientific literature contains a large amount of high-quality, reliable data, and automatically extracting data from the literature continues to be a challenge. We propose a natural language processing pipeline to capture both chemical composition and property data that allows analysis and prediction of superalloys. Within 3 h, 2531 records with both composition and property are extracted from 14,425 articles, covering γ ′ solvus temperature, density, solidus, and liquidus temperatures. A data-driven model for γ ′ solvus temperature is built to predict unexplored Co-based superalloys with high γ ′ solvus temperatures within a relative error of 0.81%. We test the predictions via synthesis and characterization of three alloys. A web-based toolkit as an online open-source platform is provided and expected to serve as the basis for a general method to search for targeted materials using data extracted from the literature.