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Computational Reverse-Engineering Analysis for Scattering Experiments for Form Factor and Structure Factor Determination ('P(q) and S(q) CREASE')
Soft Condensed Matter · Submitted 6 Dec 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In this paper we present an open-source machine learning (ML) accelerated computational method to analyze small-angle scattering profiles [I(q) vs. q] from concentrated macromolecular solutions to simultaneously obtain the form factor P(q) (e.g., dimensions of a micelle) and structure factor S(q) (e.g., spatial arrangement of the micelles) without relying on analytical models.
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In this paper we present an open-source machine learning (ML) accelerated computational method to analyze small-angle scattering profiles [I(q) vs. q] from concentrated macromolecular solutions to simultaneously obtain the form factor P(q) (e.g., dimensions of a micelle) and structure factor S(q) (e.g., spatial arrangement of the micelles) without relying on analytical models. This method builds on our recent work on Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) that has either been applied to obtain P(q) from dilute macromolecular solutions (where S(q) ~1) or to obtain S(q) from concentrated particle solution when the P(q) is known (e.g., sphere form factor). This paper's newly developed CREASE that calculates P(q) and S(q), termed as 'P(q) and S(q) CREASE' is validated by taking as input I(q) vs. q from in silico structures of known polydisperse core(A)-shell(B) micelles in solutions at varying concentrations and micelle-micelle aggregation. We demonstrate how 'P(q) and S(q) CREASE' performs if given two or three of the relevant scattering profiles - Itotal(q), IA(q), and IB(q) - as inputs; this demonstration is meant to guide experimentalists who may choose to do small-angle X-ray scattering (for total scattering from the micelles) and/or small-angle neutron scattering with appropriate contrast matching to get scattering solely from one or the other component (A or B). After validation of 'P(q) and S(q) CREASE' on in silico structures, we present our results analyzing small-angle neutron scattering profiles from a solution of core-shell type surfactant coated nanoparticles with varying extents of aggregation.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In this paper we present an open-source machine learning (ML) accelerated computational method to analyze small-angle scattering profiles [I(q) vs. q] from concentrated macromolecular solutions to simultaneously obtain the form factor P(q) (e.g., dimensions of a micelle) and structure factor S(q) (e.g., spatial arrangement of the micelles) without relying on analytical models. | contribution 1.00 | 0.91 | 0.46 | asserted 1.00 |
| s1 | This method builds on our recent work on Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) that has either been applied to obtain P(q) from dilute macromolecular solutions (where S(q) ~1) or to obtain S(q) from concentrated particle solution when the P(q) is known (e.g., sphere form factor). | background 0.99 | 0.42 | 0.06 | asserted 0.99 |
| s2 | This paper's newly developed CREASE that calculates P(q) and S(q), termed as 'P(q) and S(q) CREASE' is validated by taking as input I(q) vs. q from in silico structures of known polydisperse core(A)-shell(B) micelles in solutions at varying concentrations and micelle-micelle aggregation. | method 0.66 | 0.90 | 0.13 | asserted 1.00 |
| s3 | We demonstrate how 'P(q) and S(q) CREASE' performs if given two or three of the relevant scattering profiles - Itotal(q), IA(q), and IB(q) - as inputs; this demonstration is meant to guide experimentalists who may choose to do small-angle X-ray scattering (for total scattering from the micelles) and/or small-angle neutron scattering with appropriate contrast matching to get scattering solely from one or the other component (A or B). | method 0.56 | 0.84 | 0.07 | asserted 0.92 |
| s4 | After validation of 'P(q) and S(q) CREASE' on in silico structures, we present our results analyzing small-angle neutron scattering profiles from a solution of core-shell type surfactant coated nanoparticles with varying extents of aggregation. | contribution 0.67 | 0.93 | 0.14 | asserted 1.00 |
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Original abstract
In this paper we present an open-source machine learning (ML) accelerated computational method to analyze small-angle scattering profiles [I(q) vs. q] from concentrated macromolecular solutions to simultaneously obtain the form factor P(q) (e.g., dimensions of a micelle) and structure factor S(q) (e.g., spatial arrangement of the micelles) without relying on analytical models. This method builds on our recent work on Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) that has either been applied to obtain P(q) from dilute macromolecular solutions (where S(q) ~1) or to obtain S(q) from concentrated particle solution when the P(q) is known (e.g., sphere form factor). This paper's newly developed CREASE that calculates P(q) and S(q), termed as 'P(q) and S(q) CREASE' is validated by taking as input I(q) vs. q from in silico structures of known polydisperse core(A)-shell(B) micelles in solutions at varying concentrations and micelle-micelle aggregation. We demonstrate how 'P(q) and S(q) CREASE' performs if given two or three of the relevant scattering profiles - Itotal(q), IA(q), and IB(q) - as inputs; this demonstration is meant to guide experimentalists who may choose to do small-angle X-ray scattering (for total scattering from the micelles) and/or small-angle neutron scattering with appropriate contrast matching to get scattering solely from one or the other component (A or B). After validation of 'P(q) and S(q) CREASE' on in silico structures, we present our results analyzing small-angle neutron scattering profiles from a solution of core-shell type surfactant coated nanoparticles with varying extents of aggregation.
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