Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

被引:0
作者
Johnson, Nathan S. [1 ]
Mishra, Aashwin Ananda [1 ]
Kirsch, Dylan J. [2 ]
Mehta, Apurva [1 ]
机构
[1] SLAC Natl Accelerator Lab, Menlo Pk, CA 94025 USA
[2] Univ Maryland, Mat Sci & Engn Dept, College Pk, MD 20742 USA
关键词
vapor deposition; active learning; machine learning; THIN-FILMS; LASER;
D O I
10.3390/ma17164038
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.
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收藏
页数:15
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