scholarly journals Recognition of adsorption phase transition of polymer on surface by neural network

2019 ◽  
Vol 68 (20) ◽  
pp. 200701
Author(s):  
Li-Wang Sun ◽  
Hong Li ◽  
Peng-Jun Wang ◽  
He-Bei Gao ◽  
Meng-Bo Luo
Keyword(s):  
Neural Network ◽  
Phase Transition ◽  
Adsorption Phase

Phase‐Transition‐Induced VO 2 Thin Film IR Photodetector and Threshold Switching Selector for Optical Neural Network Applications

2021 ◽  
pp. 2001254
Author(s):  
Xi Zhou ◽  
Liang Zhao ◽  
Weili Zhen ◽  
Yinyue Lin ◽  
Chunlin Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Thin Film ◽  
Phase Transition ◽  
Network Applications ◽  
Threshold Switching ◽  
Optical Neural Network ◽  
Neural Network Applications

Finding Short-Range Parity-Time Phase-Transition Points with a Neural Network

2021 ◽  
Vol 38 (5) ◽  
pp. 051101
Author(s):  
Songju Lei ◽  
Dong Bai ◽  
Zhongzhou Ren ◽  
Mengjiao Lyu
Keyword(s):  
Neural Network ◽  
Phase Transition ◽  
Short Range ◽  
Transition Points

Anharmonic lattice dynamics of SnS across phase transition: A study using high-dimensional neural network potential

2021 ◽  
Vol 119 (6) ◽  
pp. 061902
Author(s):  
Niuchang Ouyang ◽  
Chen Wang ◽  
Zezhu Zeng ◽  
Yue Chen
Keyword(s):  
Neural Network ◽  
Phase Transition ◽  
Lattice Dynamics ◽  
High Dimensional ◽  
Anharmonic Lattice

Complex Scheduling with Potts Neural Networks

1992 ◽  
Vol 4 (6) ◽  
pp. 805-831 ◽  
Author(s):  
Lars Gislén ◽  
Carsten Peterson ◽  
Bo Söderberg
Keyword(s):  
Neural Network ◽  
Phase Transition ◽  
School System ◽  
Mean Field ◽  
Programming Approach ◽  
Scheduling Problems ◽  
Problem Complexity ◽  
Field Equations ◽  
High Quality ◽  
Mean Field Equations

In a recent paper (Gislén et al. 1989) a convenient encoding and an efficient mean field algorithm for solving scheduling problems using a Potts neural network was developed and numerically explored on simplified and synthetic problems. In this work the approach is extended to realistic applications both with respect to problem complexity and size. This extension requires among other things the interaction of Potts neurons with different number of components. We analyze the corresponding linearized mean field equations with respect to estimating the phase transition temperature. Also a brief comparison with the linear programming approach is given. Testbeds consisting of generated problems within the Swedish high school system are solved efficiently with high quality solutions as results.

scholarly journals The critical surface fugacity for self-avoiding walks on a rotated honeycomb lattice

2013 ◽  
Vol DMTCS Proceedings vol. AS,... (Proceedings) ◽  
Author(s):  
Nicholas R. Beaton
Keyword(s):  
Phase Transition ◽  
Honeycomb Lattice ◽  
Critical Surface ◽  
Similar Model ◽  
Adsorption Phase ◽  
International Audience ◽  
Self Avoiding Walk ◽  
Self Avoiding Walks

International audience In a recent paper with Bousquet-Mélou, de Gier, Duminil-Copin and Guttmann (2012), we proved that a model of self-avoiding walks on the honeycomb lattice, interacting with an impenetrable surface, undergoes an adsorption phase transition when the surface fugacity is 1+√2. Our proof used a generalisation of an identity obtained by Duminil-Copin and Smirnov (2012), and confirmed a conjecture of Batchelor and Yung (1995). Here we consider a similar model of self-avoiding walk adsorption on the honeycomb lattice, but with the impenetrable surface placed at a right angle to the previous orientation. For this model there also exists a conjecture for the critical surface fugacity, made by Batchelor, Bennett-Wood and Owczarek (1998). We adapt the methods of the earlier paper to this setting in order to prove the critical surface fugacity, but have to deal with several subtle complications which arise. This article is an abbreviated version of a paper of the same title, currently being prepared for submission.

scholarly journals THE PHASE TRANSITION IN HUMAN COGNITION

2009 ◽  
Vol 05 (01) ◽  
pp. 197-220 ◽  
Author(s):  
MICHAEL J. SPIVEY ◽  
SARAH E. ANDERSON ◽  
RICK DALE
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Phase Transition ◽  
Cognitive Psychology ◽  
Computational Neuroscience ◽  
Response Selection ◽  
Human Cognition ◽  
Natural Explanation ◽  
Linear Filters ◽  
Traditional Approaches

This article attempts to build a bridge between cognitive psychology and computational neuroscience, perhaps allowing each group to understand the other's theoretical insights and sympathize with the other's methodological challenges. In briefly discussing a collection of conceptual demonstrations, neural network and dynamical system simulations, and human experimental results, we highlight the importance of the concept of phase transition to understand cognitive function. Our goal is to show that viewing cognition as a self-organizing process (involving phase transitions, criticality, and autocatalysis) affords a more natural explanation of these data over traditional approaches inspired by a sequence of linear filters (involving detection, recognition, and then response selection).

scholarly journals Deep Neural Network Detects Quantum Phase Transition

2018 ◽  
Vol 87 (3) ◽  
pp. 033001 ◽  
Author(s):  
Shunta Arai ◽  
Masayuki Ohzeki ◽  
Kazuyuki Tanaka
Keyword(s):  
Neural Network ◽  
Phase Transition ◽  
Quantum Phase Transition ◽  
Deep Neural Network ◽  
Quantum Phase

scholarly journals Analog memristive synapse based on topotactic phase transition for high-performance neuromorphic computing and neural network pruning

2021 ◽  
Vol 7 (29) ◽  
pp. eabh0648
Author(s):  
Xing Mou ◽  
Jianshi Tang ◽  
Yingjie Lyu ◽  
Qingtian Zhang ◽  
Siyao Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Phase Transition ◽  
High Performance ◽  
Density Functional ◽  
Kinetic Monte Carlo ◽  
Random Access ◽  
Dual Mode ◽  
Neuromorphic Computing ◽  
Power Efficient ◽  
Network Pruning

Inspired by the human brain, nonvolatile memories (NVMs)–based neuromorphic computing emerges as a promising paradigm to build power-efficient computing hardware for artificial intelligence. However, existing NVMs still suffer from physically imperfect device characteristics. In this work, a topotactic phase transition random-access memory (TPT-RAM) with a unique diffusive nonvolatile dual mode based on SrCoOx is demonstrated. The reversible phase transition of SrCoOx is well controlled by oxygen ion migrations along the highly ordered oxygen vacancy channels, enabling reproducible analog switching characteristics with reduced variability. Combining density functional theory and kinetic Monte Carlo simulations, the orientation-dependent switching mechanism of TPT-RAM is investigated synergistically. Furthermore, the dual-mode TPT-RAM is used to mimic the selective stabilization of developing synapses and implement neural network pruning, reducing ~84.2% of redundant synapses while improving the image classification accuracy to 99%. Our work points out a new direction to design bioplausible memristive synapses for neuromorphic computing.

A generalized adsorption-phase transition model to describe adsorption rates in flexible metal organic framework RPM3-Zn

2016 ◽  
Vol 45 (10) ◽  
pp. 4242-4257 ◽  
Author(s):  
Angela D. Lueking ◽  
Cheng-Yu Wang ◽  
Sarmishtha Sircar ◽  
Christopher Malencia ◽  
Hao Wang ◽  
...  
Keyword(s):  
Phase Transition ◽  
Metal Organic Framework ◽  
Transition Model ◽  
Adsorption Phase ◽  
Metal Organic ◽  
Phase Transition Kinetics ◽  
Flexible Metal ◽  
Adsorption Rates ◽  
Organic Framework

The rate of adsorption to a flexible metal-organic framework is described via generalization of the Avrami theory of phase transition kinetics.

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