We review our recent work in the area of autonomous materials research, highlighting the coupling of machine learning methods and models and more problem-aware modeling. We review the general Bayesian framework for closed-loop design employed by many autonomous materials platforms. We then provide examples of our work on such platforms. We finally review our approaches to extend current statistical and ML models to better reflect problem-specific structure including the use of physics-based models and incorporation of operational considerations into the decision-making procedure.
翻译:我们审视了我们最近在自主材料研究领域开展的工作,重点介绍了机器学习方法和模型的结合以及更多有问题模式的建模。我们审视了许多自主材料平台使用的贝耶西亚封闭环设计总框架。然后我们举例说明了我们在这类平台上的工作。我们最后审视了我们扩大现有统计和多功能模型的方法,以更好地反映特定问题的结构,包括利用基于物理学的模式,以及将业务考虑纳入决策进程。