Machine learning assisted prediction of mechanical properties of graphene/aluminium nanocomposite based on molecular dynamics simulation

Liu, Jun; Zhang, Yingyan; Zhang, Yingyan; Kitipornchai, S.; Yang, Jie

doi:10.1016/j.matdes.2021.110334

Cited by 65 publications

(27 citation statements)

References 33 publications

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“…Gaussian process regression was carried out to design copper alloys with enhanced strength and electrical conductivity 15 . Regarding mechanical behavior, neural networks have been used to extract elastoplastic properties of engineering alloys 16 , yield strength and ultimate tensile strength of aluminum alloys 17 , stiffness, strength, and toughness of composites 18 , yield stress of high entropy alloys 19 , and both Young’s modulus and ultimate tensile strength of graphene–reinforced metal matrix nanocomposites 20 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning modeling for the prediction of plastic properties in metallic glasses

Amigó

Palominos

Valencia

2023

Sci Rep

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Metallic glasses are one of the most interesting mechanical materials studied in the last years, but as amorphous solids, they differ strongly from their crystalline counterparts. This matter can be addressed with the development and application of predictive techniques capable to describe the plastic regime. Here, machine learning models were employed for the prediction of plastic properties in CuZr metallic glasses. To this aim, 100 different samples were subjected to tensile tests by means of molecular dynamics simulations. A total of 17 materials properties were calculated and explored using statistical analysis. Strong correlations were found for stoichiometry, temperature, structural, and elastic properties with plastic properties. Three regression models were employed for the prediction of six plastic properties. Linear and Ridge regressions delivered the better prediction capability, with coefficients of determination above $$\sim$$ ∼ 80% for three plastic properties, whereas Lasso regression rendered lower performance, with coefficients of determination above $$\sim$$ ∼ 60% for two plastic properties. Overall, our work shows that molecular dynamics simulations together with machine learning models can provide a framework for the prediction of plastic behavior of complex materials.

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Section: Introductionmentioning

confidence: 99%

Machine learning modeling for the prediction of plastic properties in metallic glasses

Amigó

Palominos

Valencia

2023

Sci Rep

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“…This method is generic and applicable for other composites reinforced by 2D nanofillers. [18] The impact factors have an obvious influence on the mechanical properties of graphene, including internal factors (e.g., defects, dopants, chirality, and edge effect) and external factors (e.g., strain rate, system temperature). Accelerated studies about the influence of multiple factors on the mechanical properties of graphene materials can be realized by ML methods.…”

Section: Mechanical Propertiesmentioning

confidence: 99%

Recent Advances of Graphene and Related Materials in Artificial Intelligence

Huang

Zhu

2022

Advanced Intelligent Systems

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Biological brains perform real‐time processing of unstructured data with ultralow energy consumption and represent the most efficient computing systems. Emulating the working principles of biological brains brings a revolutionary breakthrough in artificial intelligence (AI), generating two kinds of imitation approaches including machine learning (ML) and neuromorphic computing (NC) with artificial neurons/synapses. Herein this review, a general description of ML methods, the concept, and principle of artificial synapses (ASs) in NC derived from biological synapses, and their relationships, is provided. Graphene, one of the most representative 2D materials, arouses considerable attention due to its unique structure and properties. The application of ML in properties prediction (electronic properties, mechanical properties, thermal properties, cytotoxicity), structure recognition (atomic structure, microscopic dimensions/shapes), inverse design (composition, structure), and task recognition (chemical recognition, motion recognition, 3D imaging) of graphene and its derivatives and composites are summarized, and corresponding methods are discussed in the case studies. Recent progress in the development and application of graphene‐based ASs (synaptic transistors and memristors) is briefly introduced, where graphene‐based materials serve as the channel materials of synaptic transistors, the memristive materials, or back electrodes of synaptic memristors. Finally, the main challenges and prospects of graphene‐incorporated ML and ASs are presented.

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“…Evidently, while the use of generative models is beneficial in scenarios where data are abundant, these models tend to fail dramatically in evolving domains, plagued by data scarcity. Keeping these limitations in mind, researchers have cleverly begun using these methods as a supplement to more conventional techniques such as MD 135 and genetic algorithms. 134 Despite the promise of these techniques, there are several challenges that lie ahead in their wide-range applications.…”

Section: Conclusion and Future Outlookmentioning

confidence: 99%

A Generative Approach to Materials Discovery, Design, and Optimization

Menon

Ranganathan

2022

ACS Omega

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Despite its potential to transform society, materials research suffers from a major drawback: its long research timeline. Recently, machine-learning techniques have emerged as a viable solution to this drawback and have shown accuracies comparable to other computational techniques like density functional theory (DFT) at a fraction of the computational time. One particular class of machine-learning models, known as “generative models”, is of particular interest owing to its ability to approximate high-dimensional probability distribution functions, which in turn can be used to generate novel data such as molecular structures by sampling these approximated probability distribution functions. This review article aims to provide an in-depth understanding of the underlying mathematical principles of popular generative models such as recurrent neural networks, variational autoencoders, and generative adversarial networks and discuss their state-of-the-art applications in the domains of biomaterials and organic drug-like materials, energy materials, and structural materials. Here, we discuss a broad range of applications of these models spanning from the discovery of drugs that treat cancer to finding the first room-temperature superconductor and from the discovery and optimization of battery and photovoltaic materials to the optimization of high-entropy alloys. We conclude by presenting a brief outlook of the major challenges that lie ahead for the mainstream usage of these models for materials research.

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Machine learning assisted prediction of mechanical properties of graphene/aluminium nanocomposite based on molecular dynamics simulation

Cited by 65 publications

References 33 publications

Machine learning modeling for the prediction of plastic properties in metallic glasses

Machine learning modeling for the prediction of plastic properties in metallic glasses

Recent Advances of Graphene and Related Materials in Artificial Intelligence

A Generative Approach to Materials Discovery, Design, and Optimization

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