ArticleChemistry of materials : a publication of the American Chemical Society2025
Autonomous Small-Angle Scattering for Accelerated Soft Material Formulation Optimization.
Article in Chemistry of materials : a publication of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials.ACS measurement science au · 2026Article
- AutoSAS: A new human-aside-the-loop paradigm for automated SAS fitting for high throughput and autonomous experimentation.APL machine learning · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The pace of soft material formulation (re)-development and design is rapidly increasing as both consumers and new legislation demand products that do less harm to the environment while maintaining high standards of performance. To meet this need, we have developed the Autonomous Formulation Lab (AFL), a platform that can automatically prepare and measure the microstructure of liquid formulations using small-angle neutron and X-ray scattering and, soon, a variety of other techniques. Here, we describe the design, philosophy, tuning, and validation of our active learning agent that guides the course of AFL experiments. We show how our extensive
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Registered trials
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