1. Introduction
Graphene, consisting of carbon atoms organized in a single or a few layers [1,2], is a planar crystal with distinctive physical and chemical properties. Due to these unique properties, it has become a prevalent area of research in the field of materials science and nanotechnology [3]. The distinctive electronic structure of graphene renders it an ideal material for high-speed electronic devices due to its exceptional electrical conductivity, transparency, and mobility.
Graphene is implemented as a semiconductor and a sensor in elec- tronics, and it is also used as transparent electrodes and field-effect transistors. Graphene’s optical properties are significant due to its high absorbance and reflectance [4], making it ideal for various applications, including solar cells, photo-detectors, optical displays, and optical com- munications [5]. The mechanical properties of graphene are also one of the important directions of its research [6], and it has excellent mechanical properties such as high strength, high toughness and high elastic modulus [7]. These properties make it has a wide range of ap- plications in the fields of composite materials, reinforced materials and biomedical applications [8–10]. Graphene exhibits abundant research opportunities with vast potential for further discovery and development in diverse fields [11,12].
Machine learning represents one of the most exciting fields in infor- mation technology today [13,14]. As computers and networks continue to advance, machine learning is becoming increasingly valuable and holds the potential to transform our everyday lives and work [15,16]. In recent years, the utilization of machine learning techniques to analyze and predict material properties has emerged as a research field at the intersection of computer science and materials science [17]. Due to significant advances in computer science and technology, machine learning has become widely applied in various domains, such as image recognition, natural language processing, pattern recognition, object detection, robotics, and more. This is because of its effective and robust learning capacity [18]. Recently, there have been notable ad- vancements in material analysis and design with the utilization of machine learning methods [19]. Takahashi et al. [20] have utilized a first-principles dataset to predict material crystal structures with a support vector machine algorithm, providing an overview of applying machine learning to predict material properties and a reference for future researchers. Additionally, it played a crucial role in developing methods for constructing crystal structures and quantifying material properties. Schut et al. [21] have proposed a structured description of crystals based on partial radial basis distributions to accurately predict the electronic states at the Fermi energy level density.
In recent years, first-principles computational methods have gained popularity in materials design as a means to shorten material de- sign time [22,23], the application of density function theory greatly increases the accuracy of the calculations as well as the reliability of the results [24–26]. Nevertheless, compared to machine learning, first-principles calculations have a relatively high time cost and can impede the progress of new materials development. In this paper, we investigate the adsorption behavior of single atoms on single vacancy graphene using first-principle calculations. The dataset was calculated using density functional theory. Mathematical models were built us- ing four different machine learning algorithms, and the most suitable model was selected based on a comparison of decision factor and root mean square error. Subsequently, the best-performing model was optimized further using a genetic algorithm and hyper-parameters were adjusted based on root mean squared error to obtain the final model.
This model was then used to make predictions for other remaining elements in the periodic table. Furthermore, validation of the accuracy of the developed model and the reliability of the prediction results were done by investigating single-atom modified single vacancy graphene surfaces. Single vacancy graphene surfaces have distinctive charac- teristics that make them attractive for use in physics and chemistry applications. However, the adsorption of atoms on these surfaces is complex and difficult to predict. By combining first principle calcula- tions with machine learning algorithms, we were able to accurately predict the adsorption energies of single atoms on single vacancy graphene. This paper is organized into the following sections: Section 2 - Calculation Details, Section 3 - Results and discussion, and Section 4 - Conclusions. 翻译成中文