Recognition of polymer configurations by unsupervised learning

Xu, Xin; Wei, Qianshi; Li, Huaping; Wang, Yuzhang; Chen, Yuguo; Jiang, Ying

doi:10.1103/physreve.99.043307

Cited by 15 publications

(15 citation statements)

References 56 publications

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“…For example, Xu et al used these techniques to study phase transitions of polymer configurations. 21 Note that the learned mapping is not necessarily directly correlated with the targeted y as such information is not included in D.…”

Section: Machine Learning In Polymer Informaticsmentioning

confidence: 99%

Potentials and challenges of polymer informatics: exploiting machine learning for polymer design

Wu¹,

Yamada²,

Hayashi³

et al. 2020

Preprint

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There has been rapidly growing demand of polymeric materials coming from different aspects of modern life because of the highly diverse physical and chemical properties of polymers. Polymer informatics is an interdisciplinary research field of polymer science, computer science, information science and machine learning that serves as a platform to exploit existing polymer data for efficient design of functional polymers. Despite many potential benefits of employing a data-driven approach to polymer design, there has been notable challenges of the development of polymer informatics

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Section: Machine Learning In Polymer Informaticsmentioning

confidence: 99%

Potentials and challenges of polymer informatics: exploiting machine learning for polymer design

Wu¹,

Yamada²,

Hayashi³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The number of fibers N has to exceed a characteristic value to observe the usual decreasing trend towards the strength of the infinite system given by Eqs. (4,5). Since at large λ the system size N controls the behaviour of the system at the critical point, it follows that N must play a decisive role also for the statistics of breaking avalanches.…”

Section: Size Dependent Avalanche Statisticsmentioning

confidence: 99%

“…The disorder of materials plays a crucial role in fracture phenomena when subject to mechanical loads. Experiments and theoretical calculations revealed that under constant or slowly varying external loads the fracture of heterogeneous materials proceeds in bursts of local breakings [1][2][3][4][5][6]. Such crackling events can be recorded in the form of acoustic signals providing insight into the microscopic dynamics of the fracture process [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

System-size-dependent avalanche statistics in the limit of high disorder

Kádár

Kun

2019

Phys. Rev. E

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We investigate the effect of the amount of disorder on the statistics of breaking bursts during the quasi-static fracture of heterogeneous materials. We consider a fiber bundle model where the strength of single fibers is sampled from a power law distribution over a finite range, so that the amount of materials' disorder can be controlled by varying the power law exponent and the upper cutoff of fibers' strength. Analytical calculations and computer simulations, performed in the limit of equal load sharing, revealed that depending on the disorder parameters the mechanical response of the bundle is either perfectly brittle where the first fiber breaking triggers a catastrophic avalanche, or it is quasi-brittle where macroscopic failure is preceded by a sequence of bursts. In the quasibrittle phase, the statistics of avalanche sizes is found to show a high degree of complexity. In particular, we demonstrate that the functional form of the size distribution of bursts depends on the system size: for large upper cutoffs of fibers' strength, in small systems the sequence of bursts has a high degree of stationarity characterized by a power law size distribution with a universal exponent. However, for sufficiently large bundles the breaking process accelerates towards the critical point of failure which gives rise to a crossover between two power laws. The transition between the two regimes occurs at a characteristic system size which depends on the disorder parameters.

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“… 39 , 40 Unsupervised and supervised learning studies identify the distinct polymer states. 41 , 42 , 43 For 2D macromolecules, unsupervised learning was used to classify graphene oxide (GO) according to the chemistry (the C/O ratio) and morphology (the mean size of flakes), which were determined by X-ray photoelectron spectroscopy and scanning electron microscopy analysis, respectively. 44 Supervised learning recognizes nanobubbles in graphene from the electronic density of states spectra, and predicts the height and width of nanobubbles.…”

Section: Introductionmentioning

confidence: 99%