Johnson Nathan S, Mishra Aashwin Ananda, Kirsch Dylan J, Mehta Apurva
SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA.
Materials Science and Engineering Department, University of Maryland, College Park, MD 20742, USA.
Materials (Basel). 2024 Aug 14;17(16):4038. doi: 10.3390/ma17164038.
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.
下一代先进材料的成分越来越复杂。合成精确的成分既耗时，而且随着成分复杂性的增加，难度呈指数级上升。经验丰富的操作人员比新手表现要好得多，但在合成参数相互关联时，仍难以始终如一地实现精确性。优化合成的时间成为探索具有科学和技术吸引力的成分复杂材料的障碍。本研究展示了一种主动学习（AL）方法，用于优化包含多达五种主要元素的薄膜合金的物理气相沉积合成。我们比较了基于高斯过程（GP）和随机森林（RF）模型的主动学习方法。表现最佳的模型能够在14次迭代中发现目标五元合金的合成参数。我们还展示了这些模型用于迁移学习任务的能力。与仅在五元样本上训练的模型相比，在较低维度系统（即三元、四元）上训练的RF和GP模型在预测准确性上有立竿见影的提高。此外，与目标成分仅共享少数几种元素的样本可用于模型预训练。我们相信，这种主动学习方法能够广泛应用，显著加速对成分复杂材料的探索。
