An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
Year: 2026
Type: article
Abstract: ABSTRACT This paper introduces a data‐driven framework designed to accelerate the property prediction, and hence the discovery and development of steels. The study leverages natural language processing and machine learning techniques to analyse a dataset containing steel compositions, (thermo)mechanical processing, and mechanical properties. By integrating unsupervized machine learning for process classification (via natural language processing‐assisted clustering) and supervised regression models for property prediction, the framework enables an efficient exploration method to study steels. A predictive accuracy of R 2 > 0.85 was achieved, with mean absolute errors <15 MPa for both yield and ultimate tensile strength. A cloud‐based graphical user interface was developed to facilitate user interaction, allowing researchers to input steel processing and composition to receive predictive insights on mechanical properties. The findings demonstrate the framework to support circular economy principles by reducing trial‐and‐error experimentation, with a view to accelerating the design of steels, and promoting sustainable steel innovation.
Source: Advanced Science
Institutions: Deakin University, Australian National University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,730
OpenAlex ID: W7151284094