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Computer vision for high-throughput materials synthesis: a tutorial for experimentalists
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DOI：10.1039/D5DD00384A.png)
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Abstract
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Advances in high-throughput instrumentation and laboratory automation are revolutionizing materials synthesis by enabling the rapid generation of large libraries of novel materials. However; efficient characterization of these synthetic libraries remains a significant bottleneck in the discovery of new materials. Traditional characterization methods are often limited to sequential analysis; making them time-intensive and cost-prohibitive when applied to large sample sets. In the same way that chemists interpret visual indicators to identify promising samples; computer vision (CV) is an efficient approach to accelerate materials characterization across varying scales when visual cues are present. CV is particularly useful in high-throughput synthesis and characterization workflows; as these techniques can be rapid; scalable; and cost-effective. Although there is a set of growing examples in the literature; we have found a lack of resources where newcomers interested in the field could get a hold of a practical way to get started. Here; we aim to fill that identified gap and present a structured tutorial for experimentalists to integrate computer vision into high-throughput materials research; providing a detailed roadmap from data collection to model validation. Specifically; we describe the hardware and software stack required for deploying CV in materials characterization; including image acquisition; annotation strategies; model training; and performance evaluation. As a case study; we demonstrate the implementation of a CV workflow within a high-throughput materials synthesis and characterization platform to investigate the crystallization of metal–organic frameworks (MOFs). By outlining key challenges and best practices; this tutorial aims to equip chemists and materials scientists with the necessary tools to harness CV for accelerating materials discovery.
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