| Abstract Scope |
Closed-loop, sequential learning is a key paradigm in autonomous materials development. Within this framework, aspects of the materials system under study are modeled, and such models are used to decide subsequent experiments to be run, results of which are fed-back to update models. Research within this nascent field has focused primarily on the modeling or decision-making aspects of this closed-loop. There are, however, other key components of the loop that deserve attention. In this talk, I will focus on two such components. First, I will describe work in autonomous materials characterization, in which rich characterization data such as microscopy images or three-dimensional reconstructions from atom probe tomography are analyzed without human intervention to encode experimental results for use to update models. Second, I will discuss work on prior knowledge formation and elicitation from experts, which is an important “step 0” within this closed-loop framework. |