Structure and Activity of Potential-Dependent Pt(110) Surface Phases Revealed from Machine-Learning Atomic Simulation

2021 ◽  
Author(s):  
Ya-Hui Fang ◽  
Dan-dan Song ◽  
Hui-xia Li ◽  
Zhi-Pan Liu
Keyword(s):  
Machine Learning ◽  
Atomic Simulation ◽  
Surface Phases ◽  
Structure And Activity

Comparison of different machine learning models for the prediction of forces in copper and silicon dioxide

2018 ◽  
Vol 20 (47) ◽  
pp. 30006-30020 ◽  
Author(s):  
Wenwen Li ◽  
Yasunobu Ando
Keyword(s):  
Machine Learning ◽  
Silicon Dioxide ◽  
Force Field ◽  
Computational Cost ◽  
High Accuracy ◽  
Simulation Approach ◽  
Learning Models ◽  
Atomic Simulation ◽  
Low Computational Cost ◽  
Machine Learning Models

Recently, the machine learning (ML) force field has emerged as a powerful atomic simulation approach because of its high accuracy and low computational cost.

scholarly journals hcp → ω phase transition mechanisms in shocked zirconium: A machine learning based atomic simulation study

2019 ◽  
Vol 162 ◽  
pp. 126-135 ◽  
Author(s):  
Hongxiang Zong ◽  
Yufei Luo ◽  
Xiangdong Ding ◽  
Turab Lookman ◽  
Graeme J. Ackland
Keyword(s):  
Machine Learning ◽  
Phase Transition ◽  
Simulation Study ◽  
Atomic Simulation ◽  
Ω Phase

scholarly journals Thermodynamic rules for zeolite formation from machine learning based global optimization

2020 ◽  
Vol 11 (37) ◽  
pp. 10113-10118
Author(s):  
Sicong Ma ◽  
Cheng Shang ◽  
Chuan-Ming Wang ◽  
Zhi-Pan Liu
Keyword(s):  
Machine Learning ◽  
Global Optimization ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Atomic Simulation ◽  
Zeolite Formation ◽  
Zeolite P

Machine learning based atomic simulation explores more than one million minima from global potential energy surface of SiAlPO system, and identifies thermodynamics rules on energetics, framework and composition for stable zeolite.

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.

CBED analysis of impurity distributions in AlN

1990 ◽  
Vol 48 (4) ◽  
pp. 214-215
Author(s):  
Daniel Callahan ◽  
G. Thomas
Keyword(s):  
Aluminum Nitride ◽  
Bulk Material ◽  
Strong Dependence ◽  
Lattice Parameter ◽  
Promising Technique ◽  
Nitride Ceramics ◽  
Surface Phases ◽  
Convergent Beam ◽  
Oxygen Impurities

Oxygen impurities may significantly influence the properties of nitride ceramics with a strong dependence on the microstructural distribution of the impurity. For example, amorphous oxygen-rich grain boundary phases are well-known to cause high-temperature mechanical strength degradation in silicon nitride whereas solutionized oxygen is known to decrease the thermal conductivity of aluminum nitride. Microanalytical characterization of these impurities by spectral methods in the AEM is complicated by reactions which form oxygen-rich surface phases not representative of the bulk material. Furthermore, the impurity concentrations found in higher quality ceramics may be too low to measure by EDS or PEELS. Consequently an alternate method for the characterization of impurities in these ceramics has been investigated.Convergent beam electron diffraction (CBED) is a promising technique for the study of impurity distributions in aluminum nitride ceramics. Oxygen is known to enter into stoichiometric solutions with AIN with a consequent decrease in lattice parameter.

Correlative transmission and high-resolution scanning electron microscopic studies on pituitary cells

1990 ◽  
Vol 48 (3) ◽  
pp. 20-21
Author(s):  
S. Tai
Keyword(s):  
Cell Types ◽  
Depth Of Field ◽  
Secretory Cells ◽  
Specific Cell ◽  
Pituitary Cells ◽  
Electron Microscopic ◽  
3 Dimensional ◽  
Microscopic Studies ◽  
Structure And Activity ◽  
Intracellular Structure

Extensive cytological and histological research, correlated with physiological experimental analysis, have been done on the anterior pituitaries of many different vertebrates which have provided the knowledge to create the concept that specific cell types synthesize, store and release their specific hormones. These hormones are stored in or associated with granules. Nevertheless, there are still many doubts - that need further studies, specially on the ultrastructure and physiology of these endocrine cells during the process of synthesis, transport and secretion, whereas some new methods may provide the information about the intracellular structure and activity in detail.In the present work, ultrastructural study of the hormone-secretory cells of chicken pituitaries have been done by using TEM as well as HR-SEM, to correlate the informations obtained from 2-dimensional TEM micrography with the 3-dimensional SEM topographic images, which have a continous surface with larger depth of field that - offers the adventage to interpretate some intracellular structures which were not possible to see using TEM.

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