Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries
This addresses the challenge of optimizing multimodal characterization workflows for materials science researchers, representing an incremental improvement in autonomous instrumentation coordination.
The authors tackled the problem of efficiently exploring combinatorial materials libraries by developing a co-orchestration approach that selects measurement modalities based on anticipated knowledge gain and cost, achieving this through a method combining variational autoencoders with multi-task Gaussian Processes. They demonstrated the framework on piezoresponse force microscopy and micro-Raman measurements of a Sm-BiFeO3 library, showing it can handle complex observables like spectra or images.
The rapid growth of automated and autonomous instrumentations brings forth an opportunity for the co-orchestration of multimodal tools, equipped with multiple sequential detection methods, or several characterization tools to explore identical samples. This can be exemplified by the combinatorial libraries that can be explored in multiple locations by multiple tools simultaneously, or downstream characterization in automated synthesis systems. In the co-orchestration approaches, information gained in one modality should accelerate the discovery of other modalities. Correspondingly, the orchestrating agent should select the measurement modality based on the anticipated knowledge gain and measurement cost. Here, we propose and implement a co-orchestration approach for conducting measurements with complex observables such as spectra or images. The method relies on combining dimensionality reduction by variational autoencoders with representation learning for control over the latent space structure, and integrated into iterative workflow via multi-task Gaussian Processes (GP). This approach further allows for the native incorporation of the system's physics via a probabilistic model as a mean function of the GP. We illustrated this method for different modalities of piezoresponse force microscopy and micro-Raman on combinatorial $Sm-BiFeO_3$ library. However, the proposed framework is general and can be extended to multiple measurement modalities and arbitrary dimensionality of measured signals. The analysis code that supports the funding is publicly available at https://github.com/Slautin/2024_Co-orchestration.