用可微机器学习加速脂质纳米粒结构解析，提升精度与效率
A differentiable machine learning small-angle X-ray scattering analysis framework for structure elucidation of lipid nanoparticles
- 构建可微分的前向模型，融合核心壳结构与神经网络代理
- 预测成本降低一万倍，支持大规模参数拟合与多解性分析
- 揭示实验数据中尺寸分布与内部结构的权衡关系，适合材料表征研究者
脂质纳米粒（LNPs）是负电荷核酸的有效递送系统，具有核壳结构。小角X射线散射（SAXS）是表征LNPs的重要技术，但从SAXS数据反推内部结构和尺寸分布属于病态逆问题，解不唯一。真实模型通常计算成本过高，难以系统探索。本文提出一种机器学习加速的可微分框架，用于异质、多分散的LNPs SAXS分析。前向模型结合核心壳粒子与高斯随机场内部结构、针对单分散SAXS图谱的神经网络代理，以及对粒子尺寸分布的可微积分层。该代理使预测成本降低四个数量级，可微性支持大规模多起点拟合与集合可辨识性分析。应用于合成与真实MC3 LNP数据表明，近乎相同的SAXS拟合可来自不同参数组合，实验拟合结果主要受尺寸分布与内部结构参数间的权衡影响。
原文摘要 · Abstract (English)
Lipid nanoparticles (LNPs) are efficient delivery systems for negatively charged nucleic acids. Their multi-component architecture yields a core-shell structure. Small-angle X-ray scattering (SAXS) is an important characterization technique for LNPs, but recovering internal structure and size distribution from SAXS is an inverse problem with non-unique solutions. Realistic models are often too expensive for systematic exploration. We introduce a machine-learning-accelerated, differentiable framework for SAXS analysis of heterogeneous, polydisperse LNPs. The forward model combines a core-shell particle with a Gaussian random-field interior, a neural surrogate for the monodisperse SAXS map, and a differentiable layer integrating over particle-size distributions. The surrogate reduces prediction cost by four orders of magnitude, while differentiability enables large-scale multi-start fitting and ensemble identifiability analysis. Applied to synthetic and experimental MC3 LNP data, the framework shows that near-identical SAXS fits can arise from distinct parameter modes, with the experimental fits dominated by a trade-off between size-distribution and interior-structure parameters.
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