scholarly journals Machine Learning-Assisted High-Throughput Molecular Dynamics Simulation of High-Mechanical Performance Carbon Nanotube Structure

Nanomaterials ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2459
Author(s):  
Yi Xiang ◽  
Koji Shimoyama ◽  
Keiichi Shirasu ◽  
Go Yamamoto
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Tensile Strength ◽  
High Throughput ◽  
Crosslink Density ◽  
Mechanical Performance ◽  
Dynamics Simulation ◽  
Optimal Structure ◽  
Outer Diameter ◽  
Nominal Tensile Strength

Carbon nanotubes (CNTs) are novel materials with extraordinary mechanical properties. To gain insight on the design of high-mechanical-performance CNT-reinforced composites, the optimal structure of CNTs with high nominal tensile strength was determined in this study, where the nominal values correspond to the cross-sectional area of the entire specimen, including the hollow core. By using machine learning-assisted high-throughput molecular dynamics (HTMD) simulation, the relationship among the following structural parameters/properties was investigated: diameter, number of walls, chirality, and crosslink density. A database, comprising the various tensile test simulation results, was analyzed using a self-organizing map (SOM). It was observed that the influence of crosslink density on the nominal tensile strength tends to gradually decrease from the outside to the inside; generally, the crosslink density between the outermost wall and its adjacent wall is highly significant. In particular, based on our calculation conditions, five-walled, armchair-type CNTs with an outer diameter of 43.39 Å and crosslink densities (between the inner wall and outer wall) of 1.38 ± 1.16%, 1.13 ± 0.69%, 1.54 ± 0.57%, and 1.36 ± 0.35% were believed to be the optimal structure, with the nominal tensile strength and nominal Young’s modulus reaching approximately 58–64 GPa and 677–698 GPa.

A Data Mining Approach to Investigate the Carbon Nanotubes Mechanical Properties via High-Throughput Molecular Simulation

2021 ◽  
Vol 1023 ◽  
pp. 29-36
Author(s):  
Yi Xiang ◽  
Go Yamamoto
Keyword(s):  
Mechanical Properties ◽  
Data Mining ◽  
Carbon Nanotubes ◽  
Tensile Strength ◽  
Molecular Simulation ◽  
High Throughput ◽  
Crosslink Density ◽  
Small Diameter ◽  
Geometrical Properties ◽  
Nominal Tensile Strength

The relationship of geometrical properties and mechanical properties of carbon nanotubes (CNTs) was investigated by using high-throughput molecular simulation. Geometrical properties such as diameter, number of walls, chirality, and crosslink density were considered. As a key factor in determining the mechanical properties of composites reinforced with CNTs, nominal tensile strength is the focus in this study, which can be calculated by fracture force divided by the full cross-sectional area including the hollow core and the wall thickness. The fracture mode, nominal tensile strength, and nominal Young’s modulus under the condition of CNTs outermost tube loading axial tensile test were evaluated. Three types of fracture modes led by different crosslink densities of CNTs were obtained. By data-mining through large amounts of datasets, we showed that CNTs with small diameter, large number of walls, and crosslinks between walls can have high nominal tensile strength. We demonstrated that zigzag-type CNTs with crosslink density of approximately 1.5% - 2.5%, armchair-type CNTs with crosslink density of approximately 3% - 4% can help improve the load transfer from the outer tube to the inner tube the most.

scholarly journals Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Taher Hajilounezhad ◽  
Rina Bao ◽  
Kannappan Palaniappan ◽  
Filiz Bunyak ◽  
Prasad Calyam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Carbon Nanotube ◽  
High Throughput ◽  
Self Assembly ◽  
Mechanical Performance ◽  
Physical Parameters ◽  
Structure Property ◽  
Processing Parameter ◽  
Physics Based Simulation

AbstractUnderstanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

scholarly journals Recognition of a Tandem Lesion by DNA Glycosylases Explored Combining Molecular Dynamics and Machine Learning

2021 ◽  
Author(s):  
Emmanuelle Bignon ◽  
Natacha Gillet ◽  
Chen-Hui Chan ◽  
Tao Jiang ◽  
Antonio Monari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Excision Repair ◽  
Double Helix ◽  
Radiation Chemistry ◽  
Dna Lesions ◽  
Dynamics Simulation ◽  
Interaction Patterns ◽  
Abasic Site ◽  
Dna Glycosylases

ABSTRACTThe combination of several closely spaced DNA lesions, which can be induced by a single radical hit, constitutes a hallmark in the DNA damage landscape and radiation chemistry. The occurrence of such tandem base lesions give rise to a strong coupling with the double helix degrees of freedom and induce important structural deformations, in contrast to DNA strands containing a single oxidized nucleobase. Although such complex lesions are known to be refractory to repair by DNA glycosylases, there is still a lack of structural evidence to rationalize these phenomena. In this contribution, we explore, by numerical modeling and molecular simulations, the behavior of the bacterial glycosylase responsible for base excision repair (MutM), specialized in excising oxidatively-damaged defects such as 7,8-dihydro-8-oxoguanine (8-oxoG). The difference in lesion recognition between a simple damage and a tandem lesions featuring an additional abasic site is assessed at atomistic resolution owing to microsecond molecular dynamics simulation and machine learning postprocessing, allowing to extensively pinpoint crucial differences in the interaction patterns of the damaged bases. This work advocates for the use of such high throughput numerical simulations for exploring the complex combinatorial chemistry of tandem DNA lesions repair and more generally multiple damaged sites of the utmost significance in radiation chemistry.

Machine Learning Interatomic Potentials for Global Optimization and Molecular Dynamics Simulation

2019 ◽  
pp. 253-288 ◽  
Author(s):  
Ivan A. Kruglov ◽  
Pavel E. Dolgirev ◽  
Artem R. Oganov ◽  
Arslan B. Mazitov ◽  
Sergey N. Pozdnyakov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Global Optimization ◽  
Molecular Dynamics Simulation ◽  
Dynamics Simulation ◽  
Interatomic Potentials
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