scholarly journals Machine learning with bond information for local structure optimizations in surface science

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

scholarly journals Disconnecting structure and dynamics in glassy thin films

2017 ◽  
Vol 114 (40) ◽  
pp. 10601-10605 ◽  
Author(s):  
Daniel M. Sussman ◽  
Samuel S. Schoenholz ◽  
Ekin D. Cubuk ◽  
Andrea J. Liu
Keyword(s):  
Thin Film ◽  
Machine Learning ◽  
Thin Films ◽  
Predictive Models ◽  
Local Structure ◽  
The Other ◽  
Microscopic Structure ◽  
Glassy Dynamics ◽  
Structure And Dynamics ◽  
Machine Learning Methods

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.

scholarly journals Quantifying the link between local structure and cellular rearrangements using information in models of biological tissues

Soft Matter ◽  
2021 ◽  
Author(s):  
Indrajit Tah ◽  
Tristan Sharp ◽  
Andrea Liu ◽  
Daniel Marc Sussman
Keyword(s):  
Machine Learning ◽  
Local Structure ◽  
Biological Tissues ◽  
Structural Features ◽  
Machine Learning Techniques ◽  
Glass Forming ◽  
Learning Techniques ◽  
Glass Forming Materials ◽  
The Relationship

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...

Latent multi-view semi-supervised classification by using graph learning

2020 ◽  
Vol 18 (05) ◽  
pp. 2050039
Author(s):  
Yanquan Huang ◽  
Haoliang Yuan ◽  
Loi Lei Lai
Keyword(s):  
Machine Learning ◽  
Local Structure ◽  
Supervised Classification ◽  
Research Direction ◽  
Unlabeled Data ◽  
Complementary Information ◽  
Graph Learning ◽  
Alternating Optimization ◽  
Learning Technique ◽  
Label Information

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.

scholarly journals Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal–Oxide Interfaces

2020 ◽  
Vol 8 ◽  
Author(s):  
Xiaoke Li ◽  
Wolfgang Paier ◽  
Joachim Paier
Keyword(s):  
Machine Learning ◽  
Metal Oxide ◽  
Surface Science ◽  
Gaussian Approximation ◽  
Force Fields ◽  
Bayesian Regression ◽  
Machine Learning Techniques ◽  
Length Scales ◽  
Oxide Interfaces ◽  
Reducible Oxides

The goal of many computational physicists and chemists is the ability to bridge the gap between atomistic length scales of about a few multiples of an Ångström (Å), i. e., 10−10 m, and meso- or macroscopic length scales by virtue of simulations. The same applies to timescales. Machine learning techniques appear to bring this goal into reach. This work applies the recently published on-the-fly machine-learned force field techniques using a variant of the Gaussian approximation potentials combined with Bayesian regression and molecular dynamics as efficiently implemented in the Vienna ab initio simulation package, VASP. The generation of these force fields follows active-learning schemes. We apply these force fields to simple oxides such as MgO and more complex reducible oxides such as iron oxide, examine their generalizability, and further increase complexity by studying water adsorption on these metal oxide surfaces. We successfully examined surface properties of pristine and reconstructed MgO and Fe3O4 surfaces. However, the accurate description of water–oxide interfaces by machine-learned force fields, especially for iron oxides, remains a field offering plenty of research opportunities.

scholarly journals Machine learning-aided analysis for complex local structure of liquid crystal polymers

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hideo Doi ◽  
Kazuaki Z. Takahashi ◽  
Kenji Tagashira ◽  
Jun-ichi Fukuda ◽  
Takeshi Aoyagi
Keyword(s):  
Machine Learning ◽  
Liquid Crystal ◽  
Local Structure ◽  
Order Parameter ◽  
Advanced Materials ◽  
Systematic Analysis ◽  
Local Structures ◽  
Specific Order ◽  
Dynamics Simulations ◽  
Technological Interest

Abstract Elucidation of mesoscopic structures of molecular systems is of considerable scientific and technological interest for the development and optimization of advanced materials. Molecular dynamics simulations are a promising means of revealing macroscopic physical properties of materials from a microscopic viewpoint, but analysis of the resulting complex mesoscopic structures from microscopic information is a non-trivial and challenging task. In this study, a Machine Learning-aided Local Structure Analyzer (ML-LSA) is developed to classify the complex local mesoscopic structures of molecules that have not only simple atomistic group units but also rigid anisotropic functional groups such as mesogens. The proposed ML-LSA is applied to classifying the local structures of liquid crystal polymer (LCP) systems, which are of considerable scientific and technological interest because of their potential for sensors and soft actuators. A machine learning (ML) model is constructed from small, and thus computationally less costly, monodomain LCP trajectories. The ML model can distinguish nematic- and smectic-like monodomain structures with high accuracy. The ML-LSA is applied to large, complex quenched LCP structures, and the complex local structures are successfully classified as either nematic- or smectic-like. Furthermore, the results of the ML-LSA suggest the best order parameter for distinguishing the two mesogenic structures. Our ML model enables automatic and systematic analysis of the mesogenic structures without prior knowledge, and thus can overcome the difficulty of manually determining the specific order parameter required for the classification of complex structures.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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