ACCURACY OF AB INITIO TECHNIQUE IN PREDICTING THE STRUCTURAL, ELECTRONIC, MECHANICAL AND ELASTIC PROPERTIES OF RHODIUM AND RUTHENIUM. DOI Open Access
Omamoke O. E. Enaroseha,

Godwin K. Agbajor,

Obed Oyibo

и другие.

Опубликована: Авг. 28, 2023

The structural, thermodynamics, electronics and mechanical properties of Rhodium andapproximately 1.6% for Ru 1.9% Rh. bulk moduli agree with other reported resultsRuthenium transition metals are analyzed using the first principle calculations in this research.depending on direction its covalent bonding while Rh withstand breaking during band three fold, two fold degenerate. And non degenerate levels represented by compared to terms elastic characteristics more prone modify GGA treat exchange – correlation function PBE functional mechanically stable as they agreed excellently results discussed literature. experimental development because anisotropy’s greater deviation from parameters respectively C44 is 2.0 221, C11 C12 0.19 238, 2C11– 2C12 is8.74 1376. also indicate that exhibits anisotropic tendencies package where elemental structures were obtained PAW pseudo potentials andreported outcome investigation shows examined elementsThe lattice deviated theoretical withWe adopted DFT solution pf Kohn Sham equation given Xcrydenunity, hence, accurately it’s stiffer tougher.

Язык: Английский

Transferable Machine Learning Interatomic Potential for Carbon Hydrogen Systems DOI Creative Commons
Somayeh Faraji, Mingjie Liu

Опубликована: Июнь 7, 2024

In this study, we developed a machine learning interatomic potential based on artificial neural networks (ANN) to model carbon-hydrogen (C-H) systems. The ANN was trained dataset of C-H clusters obtained through density functional theory (DFT) calculations. Through comprehensive evaluations against DFT results, including predictions geometries and formation energies across 0D-3D systems comprising C C-H, as well modeling various chemical processes, the demonstrated exceptional accuracy transferability. Its capability accurately predict lattice dynamics, crucial for stability assessment in crystal structure prediction, also verified phonon dispersion analysis. Notably, its computational efficiency calculating force constants facilitated exploration complex energy landscapes, leading discovery novel polymorph. These results underscore robustness versatility potential, highlighting efficacy advancing materials science by conducting precise atomistic simulations wide range materials.

Язык: Английский

Процитировано

1
Assessment of Classical Force-Fields for Graphene Mechanics DOI Creative Commons
Zhiwei Ma, Yongkang Tan, Xintian Cai

и другие.

Crystals, Год журнала: 2024, Номер 14(11), С. 960 - 960

Опубликована: Ноя. 2, 2024

The unique properties of graphene have attracted the interest researchers from various fields, and discovery has sparked a revolution in materials science, specifically field two-dimensional materials. However, synthesis’s costly complex process significantly impairs researchers’ endeavors to explore its structure experimentally. Molecular dynamics simulation is well-established useful tool for investigating graphene’s atomic dynamic behavior at nanoscale without requiring expensive experiments. accuracy molecular depends on potential functions. This work assesses performance functions available mechanical prediction. following two cases are considered: pristine pre-cracked graphene. most popular fifteen potentials been assessed. Our results suggest that diverse suitable applications. REBO Tersoff best simulating monolayer graphene, MEAM AIREBO-m recommended those with crack defects because their respective utilization electron density inclusion long-range interaction. We recommend general case classical study. might help guide selection simulations development further advanced interatomic potentials.

Язык: Английский

Процитировано

1
Constructing Two-Dimensional, Ordered Networks of Carbon–Carbon Bonds with Precision DOI Creative Commons

Jui‐Han Fu,

Dongyue Chen, Yen‐Ju Wu

и другие.

Precision Chemistry, Год журнала: 2024, Номер unknown

Опубликована: Дек. 4, 2024

Organic semiconducting nanomembranes (OSNMs), particularly carbon-based ones, are at the forefront of next-generation two-dimensional (2D) semiconductor research. These materials offer remarkable promise due to their diverse chemical properties and unique functionalities, paving way for innovative applications across advanced material sectors. Graphene stands out its extraordinary mechanical strength, thermal conductivity, superior charge transport capabilities, inspiring extensive research into other 2D carbon allotropes like graphyne graphdiyne. With high electron mobility tunable bandgap, graphdiyne is attractive power-efficient electronic devices. However, synthesizing presents significant challenges, primarily difficulty in achieving precise deterministic control over coupling monomers. This precision crucial determining material's porosity, periodicity, overall functionality. Innovative approaches have been developed address these such as strategic assembly molecular building blocks heterogeneous interfaces. Furthermore, data-driven techniques, machine learning artificial intelligence (AI), proving invaluable this field, assisting screening precursors, optimizing structural configurations, predicting novel materials. advancements essential producing durable monolayer sheets that can be integrated existing components. Despite advancements, integration technology remains complex. Achieving long-range coherence bonding configurations enhancing characteristics hurdles. Continued robust controllable synthesis techniques unlocking full potential materials, leading more efficient, faster, mechanically electronics.

Язык: Английский

Процитировано

1
Metamaterials based on hyperbolic-graphedne composite: A pathway from positive to negative refractive index at terahertz DOI
Hai Anh Nguyen, Thanh Son Pham,

Bui Son Tung

и другие.

Computational Materials Science, Год журнала: 2024, Номер 248, С. 113574 - 113574

Опубликована: Дек. 6, 2024

Язык: Английский

Процитировано

1
Machine learning approach on the prediction of mechanical characteristics of pristine, boron doped and nitrogen doped graphene DOI

P. Shahbaz,

Sumit Sharma, S. Ajori

и другие.

Physica Scripta, Год журнала: 2023, Номер 98(12), С. 126001 - 126001

Опубликована: Окт. 20, 2023

Abstract Machine Learning (ML), a subset of Artificial Intelligence has been widely applied in various domains, but it only just begun to be employed the field engineering. In present investigation, ML algorithms and artificial neural network (ANN) structures are used for first time predict mechanical properties pristine, boron-doped, nitrogen-doped graphene while also taking into account effects types vacancy defects. Fracture strain, Ultimate Tensile Strength (UTS), Young’s modulus all predicted. technique reduces computational cost required find out these materials. The training dataset models is developed using Molecular Dynamics (MD) simulations. It was shown that defects doping both had an adverse effect on characteristics. While ANN, LASSO, LASSO Lars have performed quite well at predicting features, pipeline polynomial regression best across datasets. New insights research characteristics utilizing cutting-edge techniques provided by discoveries this research.

Язык: Английский

Процитировано

2
2D materials guided interface in polymer based nanocomposites DOI
Akash Singh, Yumeng Li

AIAA SCITECH 2022 Forum, Год журнала: 2024, Номер unknown

Опубликована: Янв. 4, 2024

Two dimensional materials based nanocomposite have gained rapid technological and scientific importance in the last few decades. This hybrid material system is a combination of inorganic 2D organic/inorganic polymer that finds its application diverse set domains such as electronics, aerospace, photovoltaics, ocean technology, healthcare applications etc. The interfaces these two materials/polymers nanocomposites plays an important role determining overall properties nanocomposites. Thus, behaviour at interface needs to be understood evaluate property well can provide insights into different loading conditions. informed design help development new with enhanced wide variety applications. In this study, we are characterizing effects crystallization polyethylene, most abundant used on our planet, surface specifically graphene MoSe2 . crystal structure known guide direction polyethylene which creates anisotropy i.e. directions useful flexible skin for human-robot interaction, semiconductors Therefore, templating agents create developed nanocomposite. study investigates (2D semiconductor material) (one stiffest strongest known) polyethylene. studied fractional material-polymer progression time. We also investigated orientations lattice parameters crystallized materials. Our results indicates starts growth rate faster graphene-polyethylene compared MoSe2-polyethylene interface. For chains prefer align along armchair tend selenium valleys.

Язык: Английский

Процитировано

0
Multiphysics-informed Machine Learning for Uncertainty Quantification on Si Anode Based Battery Performance DOI

Parth Bansal,

Yumeng Li

AIAA SCITECH 2022 Forum, Год журнала: 2024, Номер unknown

Опубликована: Янв. 4, 2024

Язык: Английский

Процитировано

0
Transferable Machine Learning Interatomic Potential for Carbon Hydrogen Systems DOI
Somayeh Faraji, Mingjie Liu

Physical Chemistry Chemical Physics, Год журнала: 2024, Номер 26(34), С. 22346 - 22358

Опубликована: Янв. 1, 2024

In this study, we developed a machine learning interatomic potential based on artificial neural networks (ANN) to model carbon-hydrogen (C-H) systems. The ANN was trained dataset of C-H clusters obtained through density functional theory (DFT) calculations. Through comprehensive evaluations against DFT results, including predictions geometries and formation energies across 0D-3D systems comprising C C-H, as well modeling various chemical processes, the demonstrated exceptional accuracy transferability. Its capability accurately predict lattice dynamics, crucial for stability assessment in crystal structure prediction, also verified phonon dispersion analysis. Notably, its computational efficiency calculating force constants facilitated exploration complex energy landscapes, leading discovery novel polymorph. These results underscore robustness versatility potential, highlighting efficacy advancing materials science by conducting precise atomistic simulations wide range materials.

Язык: Английский

Процитировано

0
ACCURACY OF AB INITIO TECHNIQUE IN PREDICTING THE STRUCTURAL, ELECTRONIC, MECHANICAL AND ELASTIC PROPERTIES OF RHODIUM AND RUTHENIUM. DOI Open Access
Omamoke O. E. Enaroseha,

Godwin K. Agbajor,

Obed Oyibo

и другие.

Опубликована: Авг. 28, 2023

The structural, thermodynamics, electronics and mechanical properties of Rhodium andapproximately 1.6% for Ru 1.9% Rh. bulk moduli agree with other reported resultsRuthenium transition metals are analyzed using the first principle calculations in this research.depending on direction its covalent bonding while Rh withstand breaking during band three fold, two fold degenerate. And non degenerate levels represented by compared to terms elastic characteristics more prone modify GGA treat exchange – correlation function PBE functional mechanically stable as they agreed excellently results discussed literature. experimental development because anisotropy’s greater deviation from parameters respectively C44 is 2.0 221, C11 C12 0.19 238, 2C11– 2C12 is8.74 1376. also indicate that exhibits anisotropic tendencies package where elemental structures were obtained PAW pseudo potentials andreported outcome investigation shows examined elementsThe lattice deviated theoretical withWe adopted DFT solution pf Kohn Sham equation given Xcrydenunity, hence, accurately it’s stiffer tougher.

Язык: Английский

Процитировано

1