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

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...