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Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions
ACS Central Science ( IF 18.2 ) Pub Date : 2022-07-01 , DOI: 10.1021/acscentsci.2c00382
Christian M Heil 1 , Anvay Patil 2 , Ali Dhinojwala 2 , Arthi Jayaraman 1, 3
Affiliation  

We present a new open-source, machine learning (ML) enhanced computational method for experimentalists to quickly analyze high-throughput small-angle scattering results from multicomponent nanoparticle mixtures and solutions at varying compositions and concentrations to obtain reconstructed 3D structures of the sample. This new method is an improvement over our original computational reverse-engineering analysis for scattering experiments (CREASE) method ( ACS Materials Au 2021, 1 (22), 140−156), which takes as input the experimental scattering profiles and outputs a 3D visualization and structural characterization (e.g., real space pair-correlation functions, domain sizes, and extent of mixing in binary nanoparticle mixtures) of the nanoparticle mixtures. The new gene-based CREASE method reduces the computational running time by >95% as compared to the original CREASE and performs better in scenarios where the original CREASE method performed poorly. Furthermore, the ML model linking features of nanoparticle solutions (e.g., concentration, nanoparticles’ tendency to aggregate) to a computed scattering profile is generic enough to analyze scattering profiles for nanoparticle solutions at conditions (nanoparticle chemistry and size) beyond those that were used for the ML training. Finally, we demonstrate application of this new gene-based CREASE method for analysis of small-angle X-ray scattering results from a nanoparticle solution with unknown nanoparticle aggregation and small-angle neutron scattering results from a binary nanoparticle assembly with unknown mixing/segregation among the nanoparticles.

中文翻译:

散射实验的计算逆向工程分析 (CREASE) 与机器学习增强以确定纳米粒子混合物和溶液的结构

我们为实验人员提供了一种新的开源、机器学习 (ML) 增强型计算方法,可以快速分析来自不同成分和浓度的多组分纳米颗粒混合物和溶液的高通量小角度散射结果,从而获得样品的重建 3D 结构。这种新方法是对我们最初的散射实验计算逆向工程分析 (CREASE) 方法的改进 ( ACS Materials Au 2021 , 1(22), 140-156),它将实验散射剖面作为输入,并输出纳米粒子的 3D 可视化和结构表征(例如,实空间对相关函数、域大小和二元纳米粒子混合物中的混合程度)混合物。与原始 CREASE 相比,新的基于基因的 CREASE 方法将计算运行时间减少了 >95%,并且在原始 CREASE 方法性能不佳的情况下表现更好。此外,将纳米粒子溶液的特征(例如,浓度、纳米粒子的聚集趋势)与计算的散射分布联系起来的 ML 模型足够通用,可以分析纳米粒子溶液在条件(纳米粒子化学和尺寸)之外的散射分布。机器学习训练。最后,
更新日期:2022-07-01
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