联合协调多种仪器,揭示组合库的结构-性能关系

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Boris N. Slautin, Utkarsh Pratiush, Ilia N. Ivanov, Yongtao Liu, Rohit Pant, Xiaohang Zhang, Ichiro Takeuchi, Maxim A. Ziatdinov and Sergei V. Kalinin
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引用次数: 0

摘要

自动化和自主仪器的快速发展为多模态工具的共同协调带来了机遇,这些工具配备了多种连续检测方法或多种表征技术,可对相同的样品进行检测。例如,可以通过多个工具同时在多个位置探索组合库,或在自动合成系统中进行下游表征。在共同协调方法中,从一种模式中获得的信息应能加速其他模式的发现。相应地,协调代理应根据预期的知识收益和测量成本选择测量模式。在此,我们提出并实施了一种共同协调方法,用于对光谱或图像等复杂观测对象进行测量。该方法将变异自动编码器降维与表征学习相结合,以控制潜空间结构,并通过多任务高斯过程(GPs)集成到迭代工作流程中。这种方法还允许通过作为 GPs 平均函数的概率模型,将系统的物理特性融入其中。我们针对压电响应力显微镜和微拉曼光谱学的不同模式,对组合 Sm-BiFeO3 库进行了说明。不过,所提出的框架是通用的,可以扩展到多种测量模式和测量信号的任意维度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Co-orchestration of multiple instruments to uncover structure–property relationships in combinatorial libraries†

Co-orchestration of multiple instruments to uncover structure–property relationships in combinatorial libraries†

Co-orchestration of multiple instruments to uncover structure–property relationships in combinatorial libraries†

The rapid growth of automated and autonomous instrumentation brings forth opportunities for the co-orchestration of multimodal tools that are equipped with multiple sequential detection methods or several characterization techniques to explore identical samples. This is exemplified by combinatorial libraries that can be explored in multiple locations via multiple tools simultaneously or downstream characterization in automated synthesis systems. In co-orchestration approaches, information gained in one modality should accelerate the discovery of other modalities. Correspondingly, an orchestrating agent should select the measurement modality based on the anticipated knowledge gain and measurement cost. Herein, 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 integration into an iterative workflow via multi-task Gaussian Processes (GPs). This approach further allows for the native incorporation of the system's physics via a probabilistic model as a mean function of the GPs. We illustrate this method for different modes of piezoresponse force microscopy and micro-Raman spectroscopy on a combinatorial Sm-BiFeO3 library. However, the proposed framework is general and can be extended to multiple measurement modalities and arbitrary dimensionality of the measured signals.

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CiteScore
2.80
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