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Register a new userImprovements and considerations for size distribution retrieval from small-angle scattering data by Monte-Carlo methods
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Monte-Carlo (MC) methods, based on random updates and the trial-and-error principle, are well suited to retrieve particle size distributions from small-angle scattering patterns of dilute solutions of scatterers. The size sensitivity of size determination methods in relation to the range of scattering vectors covered by the data is discussed. Improvements are presented to existing MC methods in which the particle shape is assumed to be known. A discussion of the problems with the ambiguous convergence criteria of the MC methods are given and a convergence criterion is proposed, which also allows the determination of uncertainties on the determined size distributions.
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In statistical data assimilation (SDA) and supervised machine learning (ML), we wish to transfer information from observations to a model of the processes underlying those observations. For SDA, the model consists of a set of differential equations that describe the dynamics of a physical system. For ML, the model is usually constructed using other strategies. In this paper, we develop a systematic formulation based on Monte Carlo sampling to achieve such information transfer. Following the derivation of an appropriate target distribution, we present the formulation based on the standard Metropolis-Hasting (MH) procedure and the Hamiltonian Monte Carlo (HMC) method for performing the high dimensional integrals that appear. To the extensive literature on MH and HMC, we add (1) an annealing method using a hyperparameter that governs the precision of the model to identify and explore the highest probability regions of phase space dominating those integrals, and (2) a strategy for initializing the state space search. The efficacy of the proposed formulation is demonstrated using a nonlinear dynamical model with chaotic solutions widely used in geophysics.
In the majority of molecular optimization tasks, predictive machine learning (ML) models are limited due to the unavailability and cost of generating big experimental datasets on the specific task. To circumvent this limitation, ML models are trained on big theoretical datasets or experimental indicators of molecular suitability that are either publicly available or inexpensive to acquire. These approaches produce a set of candidate molecules which have to be ranked using limited experimental data or expert knowledge. Under the assumption that structure is related to functionality, here we use a molecular fragment-based graphical autoencoder to generate unique structural fingerprints to efficiently search through the candidate set. We demonstrate that fragment-based graphical autoencoding reduces the error in predicting physical characteristics such as the solubility and partition coefficient in the small data regime compared to other extended circular fingerprints and string based approaches. We further demonstrate that this approach is capable of providing insight into real world molecular optimization problems, such as searching for stabilization additives in organic semiconductors by accurately predicting 92% of test molecules given 69 training examples. This task is a model example of black box molecular optimization as there is minimal theoretical and experimental knowledge to accurately predict the suitability of the additives.
This paper describes the Monte Carlo simulation developed specifically for the VCS experiments below pion threshold that have been performed at MAMI and JLab. This simulation generates events according to the (Bethe-Heitler + Born) cross section behaviour and takes into account all relevant resolution-deteriorating effects. It determines the `effective solid angle for the various experimental settings which are used for the precise determination of photon electroproduction absolute cross section.
We present the first world-wide inter-laboratory comparison of small-angle X-ray scattering (SAXS) for nanoparticle sizing. The measurands in this comparison are the mean particle radius, the width of the size distribution and the particle concentration. The investigated sample consists of dispersed silver nanoparticles, surrounded by a stabilizing polymeric shell of poly(acrylic acid). The silver cores dominate the X-ray scattering pattern, leading to the determination of their radii size distribution using: i) Glatters Indirect Fourier Transformation method, ii) classical model fitting using SASfit and iii) a Monte Carlo fitting approach using McSAS. The application of these three methods to the collected datasets produces consistent mean number- and volume-weighted core radii of R$_n$ = 2.76 nm and R$_v$ = 3.20 nm, respectively. The corresponding widths of the log-normal radii distribution of the particles were $sigma_n$ = 0.65 nm and $sigma_v$ = 0.71 nm. The particle concentration determined using this method was 3.00 $pm$ 0.38 g/L (4.20 $pm$ 0.73 $times$ 10$^{-6}$ mol/L). We show that the results are slightly biased by the choice of data evaluation procedure, but that no substantial differences were found between the results from data measured on a very wide range of instruments: the participating laboratories at synchrotron SAXS beamlines, commercial and home-made instruments were all able to provide data of high quality. Our results demonstrate that SAXS is a qualified method for revealing particle size distributions in the sub-20 nm region (at least), out of reach for most other analytical methods.
We present general algorithms to convert scattering data of linear and area detectors recorded in various scattering geometries to reciprocal space coordinates. The presented algorithms work for any goniometer configuration including popular four-circle, six-circle and kappa goniometers. We avoid the use of commonly employed approximations and therefore provide algorithms which work also for large detectors at small sample detector distances. A recipe for determining the necessary detector parameters including mostly ignored misalignments is given. The algorithms are implemented in a freely available open-source package.
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