High Mechanical Energy Storage Capacity of Ultranarrow Carbon Nanowires Bundles by Machine Learning Driving Predictions

Abstract

Energy storage and renewable energy sources are critical for addressing the growing global energy demand and reducing the negative environmental impacts of fossil fuels. Carbon nanomaterials are extensively explored as high reliable, reusable, and high‐density mechanical energy storage materials. In this context, machine learning techniques, specifically machine learning potentials (MLPs), are employed to explore the elastic properties of 1D carbon nanowires (CNWs) as a promising candidate for mechanical energy storage applications. The study focuses on the elastic energy storage properties of these CNWs, utilizing MLPs trained with data from first‐principles molecular dynamics simulations. It is found that these materials exhibit an exceptionally high tensile elastic energy storage capacity, with a maximum storage density ranging from 2262 to 2680 kJ kg−1. Furthermore, it is discovered that some CNWs exhibit a superior torsional energy storage capacity compared to their tensile energy storage capacity. Overall, this research demonstrates the effectiveness of machine learning‐based computational approaches in accelerating the exploration and optimization of novel materials. It also highlights the potential of CNWs as promising candidates for future energy storage applications.

Computational understanding of the coalescence of metallic nanoparticles: a mini review

“…Besides the above empirical potentials, machine learning (ML) potentials have also been developed with the rapid development of artificial intelligence technology. 43–46 For example, neural network ML potentials developed from the DeepMD-kit package (deep potentials), which are trained from a database constructed with first-principles calculations, have been demonstrated to be accurate in predicting the structural and dynamic properties of Au, 47 Ti, 48 and Fe 49 metals, and the AgAu 50 alloy. Besides, ML potentials trained using the Gaussian approximation potential framework or the spectral neighbor analysis potential approach have also proven to possess excellent accuracy.…”

Theoretical insights into the lattice thermal conductivity and thermal expansion of CoNiFe medium-entropy alloys

“…Recently, the thermodynamic properties of high-entropy alloys have also attracted extensive research attention, particularly their thermal conductivity. 16–25 For example, Farias et al 17 calculated the lattice thermal conductivity of random solid solution Cantor alloys using the Green–Kubo method in the temperature range from 0 K to 300 K. The thermal conductivity at 300 K was found to be within 21% of the experimental results. 18 Chou et al 19 studied the thermal conductivity of Al x CoCrFeNi (0 ≤ x ≤ 2), which is significantly lower than that of the pure metal.…”

Ternary Penta-CoXS (X = Se and Te): 2D Monolayers with Coexistence of Antiferromagnetic, Auxetic, Photocatalytic, and Piezoelectric Features

“…Compared to three-dimensional (3D) bulks, two-dimensional (2D) multifunctional materials continue to be the focus of research because of their novel properties and diverse applications, such as spintronics, catalysis, electrochemical energy storage, photocatalysis, electronics nanodevices. , To date, inspired by the successful discovery and synthesis of graphene in 2004, many 2D multifunctional materials beyond graphene have been reported experimentally and theoretically . For example, black phosphorus, borophene, transition metal dichalcogenides (TMDs), and transition metal carbides (MXenes) have been proposed and synthesized experimentally, − especially the successful synthesis of 2D ferromagnetic materials, such as Cr 2 Ge 2 Te 6 and CrI 3 , ushering in the era of research on 2D magnetic materials for next-generation electronic and optoelectronic devices. − However, the discovery of 2D materials with the desired properties and superior performance is challenging. For example, 2D materials with intrinsic magnetism, photocatalytic, and superior piezoelectric properties are important for state-of-the-art miniaturized applications in piezoelectrics and photocatalysts for green hydrogen generation .…”