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In this research, the adsorption performance of individual atoms on the surface of monolayer graphene surface was systematically investigated using machine learning methods to accelerate density functional theory. The adsorption behaviors of over thirty different atoms on the graphene surface were computationally analyzed. The adsorption energy and distance were extracted as the research targets, and the basic information of atoms (such as atomic radius, ionic radius, etc.) were used as the feature values to establish the dataset. Through feature engineering selection, the corresponding input feature values for the input-output relationship were determined. By comparing different models on the dataset using five-fold cross-validation, the mathematical model that best fits the dataset was identified. The optimal model was further fine-tuned by adjusting of the best mathematical ML model. Subsequently, we verified the accuracy of the established machine learning model. Finally, the precision of the machine learning model forecasts was verified by the method of comparing and contrasting machine learning results with density functional theory. The results suggest that elements such as Zr, Ti, Sc, and Si possess some potential in controlling the interfacial reaction of graphene/aluminum composites. By using machine learning to accelerate first-principles calculations, we have further expanded our choice of research methods and accelerated the pace of studying element–graphene interactions. [ABSTRACT FROM AUTHOR]

Graphene has become an ideal reinforcement for reinforced metal matrix composites due to its excellent mechanical properties. However, the theory of graphene reinforcement in graphene/aluminum matrix composites is not yet well developed. In this paper, the effect of different temperatures on the mechanical properties of the metal matrix is investigated using a classical molecular dynamics approach, and the effects of the configuration and distribution of graphene in the metal matrix on the mechanical properties of the composites are also described in detail. It is shown that in the case of a monolayer graphene-reinforced aluminum matrix, the simulated stretching process does not break the graphene as the strain increases, but rather, the graphene and the aluminum matrix have a shearing behavior, and thus, the graphene "pulls out" from the aluminum matrix. In the parallel stretching direction, the tensile stress tends to increase with the increase of the graphene area ratio. In the vertical stretching direction, the tensile stress tends to decrease as the percentage of graphene area increases. In the parallel stretching direction, the tensile stress of the system tends to decrease as the angle between graphene and the stretching direction increases. It is important to investigate the effect of a different graphene distribution in the aluminum matrix on the mechanical properties of the composites for the design of high-strength graphene/metal matrix composites. [ABSTRACT FROM AUTHOR]

Graphene has been widely studied in many fields due to its excellent physical and chemical properties. In this paper, we focus on the electronic structure and adsorption behavior of Al atoms and X (X = B , N, Si, S) atoms co-adsorption on the surface of pure graphene and Al atom adsorption on the surface of X (X = B , N, Si, S) atoms-doped graphene systematically. The adsorption energy, adsorption distance, bond length, bond angle, and electronic structure were studied using density functional theory (DFT) theoretical calculation. The results show that the adsorption capacity of (Al, X) atoms co-adsorbed on the graphene surface is significantly improved compared to the Al atoms on the pure graphene surface. The adsorption energy of Al and B atoms co-adsorbed on the graphene surface is basically the same as that of Al atoms on the B-doped graphene surface. The calculation results also show that the adsorption capacity of Al atoms and X (X = N , Si, S) atoms co-adsorbed on the surface of graphene is better than that of Al atoms on the surface of graphene doped with X (X = N, Si, S) atoms. Compared with N atom and S atom-doped systems, the Si-doped graphene surface has a stronger adsorption capacity for Al atoms than pure graphene. However, due to the doping of N and S atoms, the adsorption capacity of Al atoms on the graphene surface is somewhat reduced. Our calculation results provide a theoretical basis for further selection of Al-based graphene composite materials. [ABSTRACT FROM AUTHOR]

In this paper, we have conducted a systematic research on the single- and double-sided adsorption models of Al atoms on the surface of graphene. The results show that the adsorption energy of single-sided Al atoms on each active site on perfect graphene is about − 1.0 eV . When a single C vacancy exists, the adsorption energy of Al atom on the surface of single-vacancy graphene is − 5.42 eV , and when two C vacancies exist, the adsorption energy of Al atom on the surface of double-vacancy graphene is − 2.76 eV . Subsequently, the adsorption behaviour of Al atoms on both sides of graphene was systematically studied. The calculation results showed that the adsorption energy of Al atoms on both sides of perfect graphene was twice that of Al atoms on one side of perfect graphene. At the same time, when Al atoms are adsorbed on both sides of graphene with vacancies, the adsorption performance is improved from − 2.25 eV to − 4.84 eV . In conclusion, the existence of vacancies is of great significance for the adsorption performance of Al atoms on the graphene surface. Our research results provide a theoretical basis for the further development of graphene-metal matrix composites. [ABSTRACT FROM AUTHOR]

In this paper, the diffusive migration behaviour of alloy atoms in aluminium matrix and different types of graphene/aluminium interfaces is systematically investigated by using a machine learning accelerated density functional theory. A small sample dataset is established by first principles calculation, the types of input and output eigenvalues are determined by feature engineering, and the number of input features for perfect interfaces, defective interfaces, and aluminium matrix are finally determined to be 6, 5, and 4 by taking into account the effects of model complexity and prediction accuracy. With a five-fold crossover and by comparing more than a dozen machine learning models, the CatBoost algorithm possesses the lowest error as well as a better coefficient of determination. We further optimized the CatBoost algorithm with further parameters and adjusted the regularization term coefficients to avoid the risk of overfitting. The impact of each feature on the model prediction results was quantitatively described by constructing a matrix of SHAP values. The best performing Catboost model was used to predict the full periodic table data, which in turn was used to screen out the elemental species that are easy to move towards the graphene/aluminium composite interface. Those alloying elements are beneficial for modifying the defective graphene in the composite by comparing the results of elemental diffusive migration in the aluminium matrix as well as at different graphene/aluminium interfaces. The results of machine learning accelerated first principles calculations can provide a theoretical basis for further development of new aluminium alloy composite. • A database was created by first-principles calculations. • The number of input features and descriptors for different targets was determined using recursive feature elimination. • Machine learning accelerated first-principles computation can provide a theoretical basis for further development of novel aluminium alloys. [ABSTRACT FROM AUTHOR]

During the preparation or processing of graphene, the emergence of structural flaws is an unavoidable circumstance, and these structural defects present a detrimental impact on the mechanical properties of graphene and its corresponding composite materials. In this paper, we use a first-principles computation to compute single-atom adsorption behavior on single vacancy graphene, from which adsorption energy and distance values are derived to create a dataset for machine learning. The comparative analysis of different machine learning models reveals that the BPNN model is best suited for the dataset. The BPNN model is enhanced by adjusting its node parameters using genetic algorithm, increasing its coefficient of determination to 0.9874 and 0.9608 for adsorption energy and distance models, respectively. The enhanced GA-BPNN model is utilized to predict the adsorption behavior of atoms across the entire periodic table onto single vacancy graphene's surface. The accuracy of the machine learning model predictions is validated through the application of elemental modifications to the graphene. Employing machine learning to expedite first-principles calculations broadens the spectrum of available research approaches while accelerating the atomic modification process of single vacancy graphene. Our results provide valuable insights into the adsorption behavior of atoms on graphene surfaces and demonstrate the potential of machine learning for accelerating first principle computations in material science. [Display omitted] • A database was created by high-throughput calculations of first-principles calculations. • The adsorption behavior was studied by machine learning accelerated first-principles calculations. • The accuracy of machine learning model predictions was verified by applying elemental modifications to defective graphene. [ABSTRACT FROM AUTHOR]

The addition of graphene in Al-based composites results in the creation of a high-content interface, which serves as the primary contributor to the reinforcement performance of the composites. Nevertheless, the reinforcing agent at the nanoscale level exhibits extremely high reactivity, making it prone to reacting with the Al matrix at the interface and producing the brittle phase Al 4 C 3. In this paper, the electronic structure and interface stability of the graphene/aluminum with alloy atoms were systematically studied using first-principles calculation based on density functional theory. The interactions of four different alloying atoms (Mg, Cu, Ti, and Ni) were analyzed, the results show that the four metal atoms exhibit varying levels of micro-alloying, and the coating of alloying atoms enhances the interfacial bonding stability. The band structure calculations of interfacial configurations with alloy atoms (Mg, Cu, Ti, Ni) indicate that Cu and Mg have little impact on the integrity of graphene layers at the interface, while Ni and Ti are detrimental to the integrity of graphene at the interface. The composites were fabricated through a process of vacuum hot-press sintering, and a series of characterization experiments were conducted to confirm the accuracy of our calculations. This study offers theoretical guidance for future development of aluminum alloy composites by exploring the impact of alloying atoms on the stability and electronic structure of the graphene/aluminum alloy interface. • The effect of alloying atom cladding on graphene/aluminum alloy interface was investigated using first principles. • The results of bond populations show that the bonding strength of several Graphene/Al alloy interfaces is: Ti > Ni > Cu > Mg. • The composites were fabricated through a process of vacuum hot-press sintering, which conducted to confirm the accuracy of our calculations. [ABSTRACT FROM AUTHOR]

Abstract: The stability of graphene oxide (GO) membranes in a sodium sulfate aqueous solution is reported. The mechanism for the stability was interpreted with the assistance of molecular dynamic (MD) simulations as follows: GO disperses uniformly in water because it is hydrated. However, the hydration of GO is weakened when Na2SO4 is present because the Na+ and SO4 2− ions are preferentially hydrated over GO. Thus the movement of GO in the Na2SO4 solution becomes restricted and it is difficult for the GO in a GO membrane to uniformly disperse in a Na2SO4 solution even with ultrasonic treatment. [Copyright &y& Elsevier]