Democratizing experimental benchmarking of materials discovery campaigns through a low-cost, self-driving optics demo

Self-driving labs are the future; however, the capital and expertise required can be daunting. We introduce the idea of a constrained, high-dimensional, multi-objective optimization task for less than $100, a square foot of desk space, and an hour... continue reading

CLSLabs-Liquid extends the minimal working example of self-driving laboratories to liquid handling and optical spectroscopy

Materials acceleration platforms have the potential to reduce the cost- and time-to-market of new materials from 20 million USD and 20 years to as little as 1 million USD and 1 year; however, the capital and expertise are often significant.... continue reading

Training a large language model to do scientific data extraction for a corpus of materials properties

A wealth of experiments and simulation results are contained in the scientific literature. Extracting it in a machine-readable format is daunting as it typically requires domain expertise, rigid schema, or intensive labor. Recently, Dunn et al.... continue reading


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