An active machine learning approach for optimal design of magnesium alloys using Bayesian optimisation

• Published in: Scientific reports Vol. 14; no. 1; p. 8299
• Main Authors: Ghorbani, M., Boley, M., Nakashima, P. N. H., Birbilis, N.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.04.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301; 639/301/1023; 639/301/1034; Alloy design; Alloys; Bayesian analysis; Bayesian optimisation; Cooling; Datasets; Design optimization; Ductility; Efficiency; Experiments; Humanities and Social Sciences; Machine learning; Magnesium; Magnesium alloys; Mechanical properties; Methods; Mg alloys; multidisciplinary; Science; Science (multidisciplinary); Sequential design strategy; Sequential design strategy; Bayesian optimisation; Mg alloys; Alloy design; Machine learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-59100-9

In the pursuit of magnesium (Mg) alloys with targeted mechanical properties, a multi-objective Bayesian optimisation workflow is presented to enable optimal Mg-alloy design. A probabilistic Gaussian process regressor model was trained through an active learning loop, while balancing the exploration and exploitation trade-off via an acquisition function of the upper confidence bound. New candidate alloys suggested by the optimiser within each iteration were appended to the training data, and the performance of this sequential strategy was validated via a regret analysis. Using the proposed approach, the dependency of the prediction error on the training data was overcome by considering both the predictions and their associated uncertainties. The method developed here, has been packaged into a web tool with a graphical user-interactive interface (GUI) that allows the proposed optimal Mg-alloy design strategy to be deployed.