自主材料研究中复杂决策的问题流模型
Problem-fluent models for complex decision-making in autonomous materials research
摘要：
我们回顾了我们最近在自主材料研究领域的工作，强调了机器学习方法和模型的耦合以及更多的问题感知建模。我们回顾了许多自主材料平台采用的闭环设计的一般贝叶斯框架。然后，我们提供了我们在这些平台上工作的例子。最后，我们回顾了我们扩展当前统计和ML模型的方法，以更好地反映特定问题的结构，包括使用基于物理的模型和将操作考虑纳入决策过程。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
