Haiyou Huang

Abstract Optimizing several properties simultaneously based on small data-driven machine learning in complex black-box scenarios can present difficulties and challenges. Here we employ a triple-objective optimization algorithm deduced from probability density functions of multivariate Gaussian distributions to optimize the γ′ volume fraction, size, and morphology in CoNiAlCr-based superalloys.

Correction to: npj Computational Materials https://doi.org/10.1038/s41524-020-00392-6, published online 18 August 2020 The original version of this article omitted the following from the Acknowledgements: “This work was supported by Beijing Top Discipline for Artificial Intelligent Science and Engineering, University of Science and Technology Beijing”. This has now been corrected in both the PDF and HTML versions of the article.

Abstract Recent progress in material data mining has been driven by high-capacity models trained on large datasets. However, collecting experimental data (real data) has been extremely costly owing to the amount of human effort and expertise required. Here, we develop a novel transfer learning strategy to address problems of small or insufficient data. This strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining procedure.