Computational Reverse-Engineering Analysis for Scattering Experiments of Assembled Binary Mixture of Nanoparticles

Abstract

In this paper, we describe a computational method for analyzing results from scattering experiments on dilute solutions of supraparticles, where each supraparticle is created by the assembly of nanoparticle mixtures. Taking scattering intensity profiles and nanoparticle mixture composition and size distributions in each supraparticle as input, this computational approach called computational reverse engineering analysis for scattering experiments (CREASE) uses a genetic algorithm to output information about the structure of the assembled nanoparticles (e.g., real space pair correlation function, extent of nanoparticle mixing/segregation, sizes of domains) within a supraparticle. We validate this method by taking as input in silico scattering intensity profiles from coarse-grained molecular simulations of a binary mixture of nanoparticles, forming a close-packed structure and testing if our computational method can correctly reproduce the nanoparticle structure observed in those simulations. We test the strengths and limitations of our method using a variety of in silico scattering intensity profiles obtained from simulations of a spherical or a cubic supraparticle comprising binary nanoparticle mixtures with varying chemistries, with and without dispersity in sizes, that exhibit well-mixed to strongly segregated structures. The strengths of the presented method include its capability to analyze scattering intensity profiles even when the wavevector q range is limited, to handily provide all of the pairwise radial distribution functions, and to correctly determine the extent of segregation/mixing of the nanoparticles assembled in complex geometries.

Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions

“…12 We consider a range of systems with A-type volume compositions of 25%v and 50%v, weak and medium nanoparticle demixing, and nanoparticle size dispersity of 9% and 20%. We select the specific systems to briefly illustrate that this gene-based CREASE method outperforms the previous original CREASE method 52 while requiring less specific inputs. The one-component nanoparticle solution systems are produced by placing nanoparticles to achieve a range of nanoparticle aggregation from disperse to strongly aggregating with nanoparticle concentrations by volume from 10%v to 50%v and nanoparticle size dispersity from 10% to 20%.…”

“…We first highlight (briefly) the key differences between the new gene-based CREASE method and our original CREASE method. 52 Additional details on this comparison are provided in the ESI Section II.…”

“…Figure 2d,f shows similar information as Figure 2c,e but for the medium demixing target structure; how well the gene-based CREASE method performs with increasing extent of particle demixing is important to study as our past work showed that the original CREASE method's performance decreased with increasing degree of demixing. 52 Because the nanoparticle system has an asymmetric composition, the A−A RDF contains the most noticeable differences in the RDF profiles from increasing the particle demixing. The gene-based CREASE with the particle size input achieves a lower RDF percent error than the original CREASE method, with an especially good match to the target A−A RDF primary peak, as quantified in ESI Figure S1.…”

“…Our past work showed that the original CREASE method performs poorly when the degree of particle demixing is high, and the increased particle size dispersity worsens that poor performance. 52 Unsurprisingly, the structure generated from the gene-based CREASE with the particle size distributions input results in the lowest RDF percent error. In addition, the gene-based CREASE with the particle size input converges to a mixture composition of 48.4 ± 2.5%v A-type; the gene-based CREASE with the particle size not input converges to an average A-type particle diameter of 220.1 ± 5.2 nm, A-type particle diameter dispersity of 20.5 ± 2.4%, average B-type particle diameter of 223.6 ± 5.4 nm, B-type particle diameter dispersity of 21.8 ± 1.1%, and a mixture composition of 43.9 ± 2.4%v A-type particle.…”

“…Besides successful application to analyzing structure in assembled polymer solutions, 47−51 this CREASE method also worked well for analyzing scattering from polydisperse nanoparticles at high nanoparticle packing fraction (near the close-packed limit) for varying particle size dispersity, mixture composition, and chemical interactions in binary nanoparticle mixtures. 52 In the case of nanoparticle systems, the key drawback of the original CREASE method was the representation of every individual as a 3D configuration (i.e., each individual stores the coordinates of all particles) and the creation of new individual configurations being dependent on stochastic particle swaps, much like RMC methods. These drawbacks led to the method being computationally slow and manifested in poor performance for the case of high packing fraction nanoparticle mixtures with strongly segregated particle domains.…”