Sutherland Duncan R, Ford Rachel, Liu Yun, Martin Tyler B, Beaucage Peter A
NIST Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA.
Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA.
APL Mach Learn. 2025 Sep;3(3):036111. doi: 10.1063/5.0271073. Epub 2025 Aug 12.
The advancement of artificial-intelligence driven autonomous experiments demands physics-based modeling and decision-making processes, not only to improve the accuracy of the experimental trajectory but also to increase trust by allowing transparent human-machine collaboration. High-quality structural characterization techniques (e.g., x ray, neutron, or static light scattering) are a particularly relevant example of this need: they provide invaluable information but are challenging to analyze without expert oversight. Here, we introduce AutoSAS, a novel framework for human-aside-the-loop automated data classification. AutoSAS leverages human-defined candidate models, high-throughput combinatorial fitting, and information-theoretic model selection to generate both classification results and quantitative structural descriptors. We implement AutoSAS in an open-source package designed for use with the Autonomous Formulation Laboratory for x-ray and neutron scattering-based optimization of multi-component liquid formulations. In a first application, we leveraged a set of expert defined candidate models to classify, refine the structure, and track transformations in a model injectable drug carrier system. We evaluated four model selection methods and benchmarked them against an optimized machine learning classifier, and the best approach was one that balanced quality of the fit and complexity of the model. AutoSAS not only corroborated the critical micelle concentration boundary identified in previous experiments but also discovered a second structural transition boundary not identified by the previous methods. These results demonstrate the potential of AutoSAS to enhance autonomous experimental workflows by providing robust, interpretable model selection, paving the way for more reliable and insightful structural characterization in complex formulations.
人工智能驱动的自主实验的发展需要基于物理的建模和决策过程，这不仅是为了提高实验轨迹的准确性，也是为了通过实现透明的人机协作来增强可信度。高质量的结构表征技术（例如，X射线、中子或静态光散射）就是这种需求的一个特别相关的例子：它们提供了宝贵的信息，但在没有专家监督的情况下进行分析具有挑战性。在这里，我们介绍了AutoSAS，一种用于人在回路外的自动数据分类的新框架。AutoSAS利用人为定义的候选模型、高通量组合拟合和信息论模型选择来生成分类结果和定量结构描述符。我们在一个开源软件包中实现了AutoSAS，该软件包设计用于与自主配方实验室配合使用，以基于X射线和中子散射对多组分液体制剂进行优化。在首次应用中，我们利用一组专家定义的候选模型对模型注射药物载体系统进行分类、细化结构并跟踪转变。我们评估了四种模型选择方法，并将它们与优化的机器学习分类器进行基准测试，最佳方法是一种平衡拟合质量和模型复杂性的方法。AutoSAS不仅证实了先前实验中确定的临界胶束浓度边界，还发现了先前方法未识别的第二个结构转变边界。这些结果证明了AutoSAS通过提供强大、可解释的模型选择来增强自主实验工作流程的潜力，为在复杂配方中进行更可靠、更有洞察力的结构表征铺平了道路。
