Machine learning enables precise holographic characterization of colloidal materials in real time
Machine learning enables precise holographic characterization of colloidal materials in real time
复制标题
机器学习能够实时精确地表征胶体材料
DOI：
10.1039/d2sm01283a
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发表时间：
2023
期刊：
影响因子：
3.4
通讯作者：
Grier, David G.
中科院分区：
文献类型：
--
作者：
Altman, Lauren E.;Grier, David G.
全息颗粒表征使用在线全息视频显微镜来跟踪和表征分散在其天然流体介质中的单个胶体颗粒。应用范围从统计物理学的基础研究到生物制药和医学诊断测试的产品开发。全息图中编码的信息可以通过拟合基于光散射洛伦兹-米氏理论的生成模型来提取。将全息图分析视为高维反问题非常成功，传统的优化算法可以为典型粒子的位置提供纳米级精度，并为其尺寸和折射率提供千分之一的精度。机器学习以前已用于通过检测多粒子全息图中感兴趣的特征并估计粒子的位置和属性以进行后续细化来自动化全息粒子表征。这项研究提出了一种更新的端到端神经网络解决方案，称为 CATCH（全息胶体表征和跟踪），其预测对于许多现实世界的高吞吐量应用来说足够快速、精确和准确，并且可以为最苛刻的应用可靠地引导传统优化算法。 CATCH 能够学习适合 200 kB 的 Lorenz-Mie 理论的表示形式，这暗示了开发一种大大简化的小物体光散射公式的可能性。
Holographic particle characterization uses in-line holographic video microscopy to track and characterize individual colloidal particles dispersed in their native fluid media. Applications range from fundamental research in statistical physics to product development in biopharmaceuticals and medical diagnostic testing. The information encoded in a hologram can be extracted by fitting to a generative model based on the Lorenz–Mie theory of light scattering. Treating hologram analysis as a high-dimensional inverse problem has been exceptionally successful, with conventional optimization algorithms yielding nanometer precision for a typical particle's position and part-per-thousand precision for its size and index of refraction. Machine learning previously has been used to automate holographic particle characterization by detecting features of interest in multi-particle holograms and estimating the particles' positions and properties for subsequent refinement. This study presents an updated end-to-end neural-network solution called CATCH (Characterizing and Tracking Colloids Holographically) whose predictions are fast, precise, and accurate enough for many real-world high-throughput applications and can reliably bootstrap conventional optimization algorithms for the most demanding applications. The ability of CATCH to learn a representation of Lorenz–Mie theory that fits within a diminutive 200 kB hints at the possibility of developing a greatly simplified formulation of light scattering by small objects.