scholarly journals Machine learning enhanced local structure search

Author(s):  
Ralf Meyer ◽  
Andreas Hauser
2022 ◽  
Vol 35 ◽  
pp. 100771
Author(s):  
Eric Musa ◽  
Francis Doherty ◽  
Bryan R Goldsmith

2017 ◽  
Vol 114 (40) ◽  
pp. 10601-10605 ◽  
Author(s):  
Daniel M. Sussman ◽  
Samuel S. Schoenholz ◽  
Ekin D. Cubuk ◽  
Andrea J. Liu

Nanometrically thin glassy films depart strikingly from the behavior of their bulk counterparts. We investigate whether the dynamical differences between a bulk and thin film polymeric glass former can be understood by differences in local microscopic structure. Machine learning methods have shown that local structure can serve as the foundation for successful, predictive models of particle rearrangement dynamics in bulk systems. By contrast, in thin glassy films, we find that particles at the center of the film and those near the surface are structurally indistinguishable despite exhibiting very different dynamics. Next, we show that structure-independent processes, already present in bulk systems and demonstrably different from simple facilitated dynamics, are crucial for understanding glassy dynamics in thin films. Our analysis suggests a picture of glassy dynamics in which two dynamical processes coexist, with relative strengths that depend on the distance from an interface. One of these processes depends on local structure and is unchanged throughout most of the film, while the other is purely Arrhenius, does not depend on local structure, and is strongly enhanced near the free surface of a film.


2020 ◽  
Vol 7 (15) ◽  
pp. 2000992
Author(s):  
Alexander T. Egger ◽  
Lukas Hörmann ◽  
Andreas Jeindl ◽  
Michael Scherbela ◽  
Veronika Obersteiner ◽  
...  

Soft Matter ◽  
2021 ◽  
Author(s):  
Indrajit Tah ◽  
Tristan Sharp ◽  
Andrea Liu ◽  
Daniel Marc Sussman

Machine learning techniques have been used to quantify the relationship between local structural features and variations in local dynamical activity in disordered glass-forming materials. To date these methods have been...


Author(s):  
Yanquan Huang ◽  
Haoliang Yuan ◽  
Loi Lei Lai

Multi-view learning is a hot research direction in the field of machine learning and pattern recognition, which is attracting more and more attention recently. In the real world, the available data commonly include a small number of labeled samples and a large number of unlabeled samples. In this paper, we propose a latent multi-view semi-supervised classification method by using graph learning. This work recovers a latent intact representation to utilize the complementary information of the multi-view data. In addition, an adaptive graph learning technique is adopted to explore the local structure of this latent intact representation. To fully use this latent intact representation to discover the label information of the unlabeled data, we consider to unify the procedures of computing the latent intact representation and the labels of unlabeled data as a whole. An alternating optimization algorithm is designed to effectively solve the optimization of the proposed method. Extensive experimental results demonstrate the effectiveness of our proposed method.


2020 ◽  
Vol 153 (23) ◽  
pp. 234116
Author(s):  
Estefanía Garijo del Río ◽  
Sami Kaappa ◽  
José A. Garrido Torres ◽  
Thomas Bligaard ◽  
Karsten Wedel Jacobsen

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Patrick Avery ◽  
Xiaoyu Wang ◽  
Corey Oses ◽  
Eric Gossett ◽  
Davide M. Proserpio ◽  
...  

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