Machine learning in materials modeling—fundamentals and the opportunities in 2D materials

Das, Shreeja; Pegu, Hansraj; Sahu, Κ. K.; Nayak, Ameeya Kumar; Ramakrishna, Seeram; Datta, Debasish; Swayamjyoti, S.

doi:10.1016/b978-0-12-818475-2.00019-2

Cited by 11 publications

(6 citation statements)

References 66 publications

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“…Identifying the appropriate materials combinations and creating methods of measurement to study relevant properties is challenging and could be tackled through different computational techniques. One of the techniques among them is Machine Learning (ML), which can help drive forward this field of research with data generated from past successes and failures [47]. The last decade has seen a rise in the work of machine learning materials discovery.…”

Section: Role Of Machine Learning-driven Atomic Simulations In Batter...mentioning

confidence: 99%

Opportunities and challenges for 2D heterostructures in battery applications: a computational perspective

Browne¹,

Waghmare²,

Singh³

2022

Nanotechnology

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With an increasing demand for large-scale energy storage systems, there is a need for novel electrode materials to store energy in batteries efficiently. 2D materials are promising as electrode materials for such battery applications. Despite their excellent properties, none of the available single-phase 2D materials offers a combination of properties required for maximizing energy density, power density, and cycle life. This article discusses how stacking of distinct 2D materials into a 2D heterostructure may open up new possibilities for battery electrodes, combining favourable characteristics and overcoming the drawbacks of constituent 2D layers. Computational studies are crucial to advancing this field rapidly with first-principles simulations of various 2D heterostructures forming the basis for such investigations that offer insights into processes that are hard to determine otherwise. We present a perspective on the current methodology, along with a review of the known 2D heterostructures as anodes and their potential for Li and Na-ion battery applications. 2D heterostructures showcase excellent tunability with different compositions. However, each of them has distinct properties, with its own set of challenges and opportunities for application in batteries. We highlight the current status and prospects to stimulate research into designing new 2D heterostructures for battery applications.

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Section: Role Of Machine Learning-driven Atomic Simulations In Batter...mentioning

confidence: 99%

Opportunities and challenges for 2D heterostructures in battery applications: a computational perspective

Browne¹,

Waghmare²,

Singh³

2022

Nanotechnology

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“…With ML, sensors can recognize surroundings and adapt to individual user preferences. Enhancing the performance, dependability, and practicality of 2D materials-based sensors through the integration of ML is a promising strategy. − Initially, pattern recognition and data processing are the uses of ML algorithms. Unprocessed sensor data can be complicated and noisy, such as electrical conductivity changes in reaction to stress or gas exposure.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Assisted Enhancement in a Two-Dimensional Material’s Sensing Performance

Das,

Mazumdar,

Khondakar

et al. 2024

ACS Appl. Nano Mater.

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materials have shown a dramatic increase in use in recent years due to their exceptional characteristics, which make them perfect for a wide range of sensing applications. However, achieving optimal sensing performance in 2D material-based sensors often poses challenges owing to material limitations and environmental factors. The combination of machine learning (ML) algorithms with 2D materials offers a way to maximize selectivity, sensitivity, and overall sensor dependability. This study starts by looking at the basic characteristics of many 2D materials and their uses in sensing, such as graphene and transition metal dichalcogenides (TMDs). It then explores the difficulties encountered by conventional sensing techniques and shows how ML techniques overcome these difficulties. A thorough examination of the various ML methods used with 2D materials is provided, along with an explanation of their functions in data processing, pattern identification, and real-time adaptive sensing. This paper also discusses how ML might lead to better performance measures including lower false positive rates and higher accuracy. Comprehensive analysis is done on case studies that demonstrate effective implementations in many sensing domains, such as industrial applications, environmental monitoring, and healthcare. In conclusion, this article discusses prospects for the future, highlighting how ML-assisted 2D materials-based sensors are developing and how they might transform sensing technologies in a variety of fields.

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“…Machine learning is an important subset of AI [ 28 ] suitable for high-dimensional data problems with linear and nonlinear properties and has succeeded in many applications concerning smart materials [ 29 , 30 ]. Materials discovery is one of the widespread applications where artificial neural networks (ANNs), support vector machines (SVMs), and Bayesian methods can be used for designing new materials such as guanidinium ionic liquids [ 31 ] or crystals [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Review of Neural Network Modeling of Shape Memory Alloys

Hmede

Chapelle

Lapusta

2022

Sensors

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Shape memory materials are smart materials that stand out because of several remarkable properties, including their shape memory effect. Shape memory alloys (SMAs) are largely used members of this family and have been innovatively employed in various fields, such as sensors, actuators, robotics, aerospace, civil engineering, and medicine. Many conventional, unconventional, experimental, and numerical methods have been used to study the properties of SMAs, their models, and their different applications. These materials exhibit nonlinear behavior. This fact complicates the use of traditional methods, such as the finite element method, and increases the computing time necessary to adequately model their different possible shapes and usages. Therefore, a promising solution is to develop new methodological approaches based on artificial intelligence (AI) that aims at efficient computation time and accurate results. AI has recently demonstrated some success in efficiently modeling SMA features with machine- and deep-learning methods. Notably, artificial neural networks (ANNs), a subsection of deep learning, have been applied to characterize SMAs. The present review highlights the importance of AI in SMA modeling and introduces the deep connection between ANNs and SMAs in the medical, robotic, engineering, and automation fields. After summarizing the general characteristics of ANNs and SMAs, we analyze various ANN types used for modeling the properties of SMAs according to their shapes, e.g., a wire as an actuator, a wire with a spring bias, wire systems, magnetic and porous materials, bars and rings, and reinforced concrete beams. The description focuses on the techniques used for NN architectures and learning.

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Machine learning in materials modeling—fundamentals and the opportunities in 2D materials

Cited by 11 publications

References 66 publications

Opportunities and challenges for 2D heterostructures in battery applications: a computational perspective

Opportunities and challenges for 2D heterostructures in battery applications: a computational perspective

Machine Learning Assisted Enhancement in a Two-Dimensional Material’s Sensing Performance

Review of Neural Network Modeling of Shape Memory Alloys

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