Multivariate curve resolution combined with estimation by cosine similarity mapping of analytical data

Nagai, Yuya; Katayama, Kenji

doi:10.1039/d1an00362c

Cited by 8 publications

(2 citation statements)

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“…Clusters formed out of MLA units are more similar to the reference based on cosine similarity. We note that the cosine similarity differentiates between the clusters emerging over time under various system compositions and it is widely used to compare spectra. − Nevertheless, other similarity metrics have been assessed and they are provided in the Supporting Information, Section SVIII, pp. 26–32.…”

Section: Results and Discussionmentioning

confidence: 99%

Understanding Metal–Organic Framework Nucleation from a Solution with Evolving Graphs

et al. 2022

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A mechanistic understanding of metal−organic framework (MOF) synthesis and scale-up remains underexplored due to the complex nature of the interactions of their building blocks. In this work, we investigate the collective assembly of building units at the early stages of MOF nucleation, using MIL-101(Cr) as a prototypical example. Using large-scale molecular dynamics simulations, we observe that the choice of solvent (water and N,N-dimethylformamide), the introduction of ions (Na + and F − ) and the relative populations of MIL-101(Cr) half-secondary building unit (half-SBU) isomers have a strong influence on the cluster formation process. Additionally, the shape, size, nucleation and growth rates, crystallinity, and short and long-range order largely vary depending on the synthesis conditions. We evaluate these properties as they naturally emerge when interpreting the self-assembly of MOF nuclei as the time evolution of an undirected graph. Solution-induced conformational complexity and ionic concentration have a dramatic effect on the morphology of clusters emerging during assembly. While pure solvents lead to the rapid formation of a small number of large clusters, the presence of ions in aqueous solutions results in smaller clusters and slower nucleation. This diversity is captured by the key features of the graph representation. Principle component analysis on graph properties reveals that only a small number of molecular descriptors is needed to deconvolute MOF self-assembly. Descriptors such as the average coordination number between half-SBUs and fractal dimension are of particulalr interest as they can be can be followed experimentally by techniques like by time-resolved spectroscopy. Ultimately, graph theory emerges as an approach that can be used to understand complex processes revealing molecular descriptors accessible by both simulation and experiment.

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Section: Results and Discussionmentioning

confidence: 99%

Understanding Metal–Organic Framework Nucleation from a Solution with Evolving Graphs

et al. 2022

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“…Y data were predicted using the descriptors, X using various ML models. The number of descriptors was reduced by understanding the dominant descriptors, based on the cosine-similarity map, 23 least absolute shrinkage and selection operator (LASSO), partial least squares (PLS), and stepwise regression. An ML model was constructed by support vector regression (SVR), Gaussian process regression (GPR), decision tree regression (DT), and random forest regression (RF).…”

Section: Methodsmentioning

confidence: 99%

Prediction of the photoelectrochemical performance of hematite electrodes using analytical data

Nagai

Katayama

2022

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Application of a wavelength angle mapper for variable selection in iterative optimization technology predictions of drug content in pharmaceutical powder mixtures

Rish,

Henson,

Velez-Silva

et al. 2023

International Journal of Pharmaceutics

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Multivariate curve resolution combined with estimation by cosine similarity mapping of analytical data

Abstract: We developed a multivariate curve resolution (MCR) calculation combined with the mapping of cosine similarity (cos-s) for multiple mixture spectra of chemicals. The cos-s map was obtained by calculating the...

Cited by 8 publications

References 41 publications

Understanding Metal–Organic Framework Nucleation from a Solution with Evolving Graphs

Understanding Metal–Organic Framework Nucleation from a Solution with Evolving Graphs

Prediction of the photoelectrochemical performance of hematite electrodes using analytical data

Application of a wavelength angle mapper for variable selection in iterative optimization technology predictions of drug content in pharmaceutical powder mixtures

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