Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

• DOI: 10.1002/adma.202201345
• Publication Date: 2022-03-13T04:03:15Z
• Authors: Maxim A. Ziatdinov; Yongtao Liu; Anna N. Morozovska; Eugene A. Eliseev; Xiaohang Zhang; Ichiro Takeuchi; Sergei V. Kalinin
• Keywords: 0301 basic medicine; Condensed Matter - Materials Science; Materials Science (cond-mat.mtrl-sci); FOS: Physical sciences; 02 engineering and technology; Machine Learning; Microscopy, Electron; 03 medical and health sciences; Physics - Data Analysis, Statistics and Probability; Humans; 0210 nano-technology; Data Analysis, Statistics and Probability (physics.data-an)

AbstractMachine learning is rapidly becoming an integral part of experimental physical discovery via automated and high‐throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. Here, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human‐driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration‐induced phase transitions in combinatorial libraries of Sm‐doped BiFeO3 using piezoresponse force microscopy, but it is straightforward to extend it to higher‐dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available.