The thermoelastic properties of monolayer covalent organic frameworks studied by machine-learning molecular dynamics

Wang, Bing; Ying, Penghua; Zhang, Jin

doi:10.1039/d3nr04509a

Cited by 5 publications

(2 citation statements)

References 74 publications

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“…Because the thermal fluctuation becomes more significant as the temperature rises, a larger atomic displacement is observed in the HKUST-1 at higher temperatures. The larger atomic displacements observed at higher temperatures can also be verified by the larger time-averaged root-mean square (RMS) displacements of atoms 55 and the wider distribution of the radial distribution functions (RDFs) of HKUST-1 at higher temperatures as shown in Fig. 6b and c, respectively.…”

Section: Resultsmentioning

confidence: 63%

Thermomechanical properties of metal–organic framework HKUST-1 crystals

Wang,

Ke,

Zhang

2024

J. Mater. Chem. A

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Section: Resultsmentioning

confidence: 63%

Thermomechanical properties of metal–organic framework HKUST-1 crystals

Wang,

Ke,

Zhang

2024

J. Mater. Chem. A

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“…These values are around five- and two-fold lower than the corresponding values of graphene [ 36 ] and the quasi-hexagonal C 60 monolayer [ 42 ], suggesting the low lattice thermal conductivity of the goldene system. To better evaluate this aspect, MLIPs can provide a highly accurate understanding [ 43 , 44 , 45 , 46 , 47 , 48 ]. In Figure 1 f, the MTP-based NEMD predictions for the length effects on the room temperature phononic thermal conductivity of the goldene nanosheet along the x and y directions are plotted.…”

Section: Resultsmentioning

confidence: 99%

Goldene: An Anisotropic Metallic Monolayer with Remarkable Stability and Rigidity and Low Lattice Thermal Conductivity

Mortazavi

2024

Materials

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In a recent breakthrough in the field of two-dimensional (2D) nanomaterials, the first synthesis of a single-atom-thick gold lattice of goldene has been reported through an innovative wet chemical removal of Ti3C2 from the layered Ti3AuC2. Inspired by this advancement, in this communication and for the first time, a comprehensive first-principles investigation using a combination of density functional theory (DFT) and machine learning interatomic potential (MLIP) calculations has been conducted to delve into the stability, electronic, mechanical and thermal properties of the single-layer and free-standing goldene. The presented results confirm thermal stability at 700 K as well as remarkable dynamical stability of the stress-free and strained goldene monolayer. At the ground state, the elastic modulus and tensile strength of the goldene monolayer are predicted to be over 226 and 12 GPa, respectively. Through validated MLIP-based molecular dynamics calculations, it is found that at room temperature, the goldene nanosheet can exhibit anisotropic tensile strength over 9 GPa and a low lattice thermal conductivity around 10 ± 2 W/(m.K), respectively. We finally show that the native metallic nature of the goldene monolayer stays intact under large tensile strains. The combined insights from DFT and MLIP-based results provide a comprehensive understanding of the stability, mechanical, thermal and electronic properties of goldene nanosheets.

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Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

Mortazavi

2024

Advanced Energy Materials

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This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification of candidates with desirable properties. Recently, the development of highly accurate ML interatomic potentials and generative models has not only improved the robust prediction of physical properties, but also significantly accelerated the discovery of materials. In the past couple of years, ML methods have enabled high‐precision first‐principles predictions of electronic and optical properties for large systems, providing unprecedented opportunities in materials science. Furthermore, ML‐assisted microstructure reconstruction and physics‐informed solutions for partial differential equations have facilitated the understanding of microstructure–property relationships. Most recently, the seamless integration of various ML platforms has led to the emergence of autonomous laboratories that combine quantum mechanical calculations, large language models, and experimental validations, fundamentally transforming the traditional approach to novel materials synthesis. While highlighting the aforementioned recent advances, existing challenges are also discussed. Ultimately, ML is expected to fully integrate atomic‐scale simulations, reverse engineering, process optimization, and device fabrication, empowering autonomous and generative energy system design. This will drive transformative innovations in energy conversion, storage, and harvesting technologies.

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The thermoelastic properties of monolayer covalent organic frameworks studied by machine-learning molecular dynamics

Abstract: The machine-learned neuroevolution potential with high efficiency and accuracy has been developed to study the elastic properties of finite-sized monolayer covalent organic frameworks at various temperatures.

Cited by 5 publications

References 74 publications

Thermomechanical properties of metal–organic framework HKUST-1 crystals

Thermomechanical properties of metal–organic framework HKUST-1 crystals

Goldene: An Anisotropic Metallic Monolayer with Remarkable Stability and Rigidity and Low Lattice Thermal Conductivity

Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

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