# Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries

arXiv: https://arxiv.org/abs/2402.02198 · HTML: https://mlsanity.com/paper/2402.02198/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization](https://mlsanity.com/paper/2111.05040/) (2021)
- [Good practices for Bayesian Optimization of high dimensional structured spaces](https://mlsanity.com/paper/2012.15471/) (2020)
- [Multi-Fidelity Cost-Aware Bayesian Optimization](https://mlsanity.com/paper/2211.02732/) (2022)
- [Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning](https://mlsanity.com/paper/2205.15458/) (2022)
- [A spectrum of physics-informed Gaussian processes for regression in engineering](https://mlsanity.com/paper/2309.10656/) (2023)
- [Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy](https://mlsanity.com/paper/2310.05018/) (2023)
- [Scalable Multi-Agent Lab Framework for Lab Optimization](https://mlsanity.com/paper/2208.09099/) (2022)

## Built on by (most cited first, 4 of 4)

- [SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions](https://mlsanity.com/paper/2409.12295/) (2024)
- [Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization](https://mlsanity.com/paper/2506.20056/) (2025)
- [Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy](https://mlsanity.com/paper/2603.16715/) (2026)
- [Compressing the Validation Bottleneck: An Agentic Self-Driving Lab for Scientific Discovery](https://mlsanity.com/paper/2607.04508/) (2026)

## Abstract (arXiv)

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-BiFeO3 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/2024Co-orchestration.