Machine learning real space microstructure characteristics from scattering data

Jones, Matthew; Clarke, Nigel

doi:10.1039/d1sm00818h

Cited by 1 publication

(2 citation statements)

References 19 publications

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“…Artificial neural network (ANN) has also been proven successful in designing superhydrophobic polymers with optimized contact angle/Laplace pressure balance . ML image analysis models are also applied to study the phase separation mechanisms in polymer blends . In Table , you can find the summary of the research papers mentioned above.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Prediction of Janus/Core–Shell Morphology in Polymer Particles: A Machine-Learning Approach

2023

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The majority of research on Janus particles prepared by solvent evaporation-induced phase separation technique uses models based on interfacial tension or free energy to predict Janus/core−shell morphology. Data-driven predictions, in contrast, utilize multiple samples to identify patterns and outliers. Using machine-learning algorithms and explainable artificial intelligence (XAI) analysis, we developed a model based on a 200-instance data set to predict particle morphology. As model features, simplified molecular input line entry system syntax identifies explanatory variables, including cohesive energy density, molar volume, the Flory−Huggins interaction parameter of polymers, and the solvent solubility parameter. Our most accurate ensemble classifiers predict morphology with an accuracy of 90%. In addition, we employ innovative XAI tools to interpret system behavior, suggesting phase-separated morphology to be most affected by solvent solubility, polymer cohesive energy difference, and blend composition. While polymers with cohesive energy densities above a certain threshold favor the core−shell structure, systems with weak intermolecular interactions favor the Janus structure. The correlation between molar volume and morphology suggests that increasing the size of polymer repeating units favors Janus particles. Additionally, the Janus structure is preferred when the Flory−Huggins interaction parameter exceeds 0.4. XAI analysis introduces feature values that generate the thermodynamically low driving force of phase separation, resulting in kinetically stable morphologies as opposed to thermodynamically stable ones. The Shapley plots of this study also reveal novel methods for creating Janus or core−shell particles based on solvent evaporation-induced phase separation by selecting feature values that strongly favor a given morphology.

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Section: Introductionmentioning

confidence: 99%

“…31 ML image analysis models are also applied to study the phase separation mechanisms in polymer blends. 32 In Table 1, you can find the summary of the research papers mentioned above. Although data-driven ML models in polymer informatics are in their early stages, the results show a bright outlook for the near future.…”

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confidence: 99%