Engineering topics

Chen, Liangyao

Publications and source records attributed to Chen, Liangyao.

4 records

DOE OSTI2021

Neural network potential for Zr–Rh system by machine learning

Zr–Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr–Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. Here, the results show that the structural features obtained from the neural network method are in good agreement with the cases in ab initio molecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr 77 Rh 23 . Our study lays a foundation for exploring the complex structures in amorphous Zr 77 Rh 23 , which is of great significance for the design and practical application.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

A deep learning interatomic potential developed for atomistic simulation of carbon materials

Interatomic potentials based on neural-network machine learning method have attracted considerable attention in recent years owing to their outstanding ability to balance the accuracy and efficiency in atomistic simulations. In this work, a neural-network potential (NNP) for carbon is generated to simulate the structural properties of various carbon structures. The potential is trained using a database consisting of crystalline and liquid structures obtained by the first-principles density functional theory (DFT) calculations. The developed potential accurately predicts the energies and forces in crystalline and liquid carbon structures, the energetic stability of defected graphene, and the structures of amorphous carbon as the function of density. As a result, the excellent accuracy and transferability of the NNP provide a promising tool for accurate atomistic simulations of various carbon materials with faster speed and much lower cost.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Luminescence mechanism in hydrogenated silicon quantum dots with a single oxygen ligand

Though photoluminescence (PL) of Si quantum dots (QDs) has been known for decades and both theoretical and experimental studies have been extensive, their luminescence mechanism has not been elaborated. Several models have been proposed to explain the mechanism. A deep insight into the origin of light emissions in Si QDs is necessary. This work calculated the ground- and excited state properties of hydrogenated Si QDs with various diameters, including full hydrogen passivation, single Si=O ligands, single epoxide and coexisting Si=O and epoxide structures in order to investigate the dominant contribution states for luminescence. The results revealed that even a single oxygen atom in hydrogenated Si QDs can dramatically change their electronic and optical properties. Intriguingly, we found that a size-independent emission, the strongest among all possible emissions, was induced by the single Si=O passivated Si-QDs. In non-oxidized Si-QDs exhibiting a core-related size-tunable emission, the luminescence properties can be modulated by the ligands of Si QDs, and a very small number of oxygen ligands can strongly influence the luminescence of nanocrystalline silicon. Our findings deepen the understanding of the PL mechanism of Si QDs and can further promote the development of silicon-based optoelectronic devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Manipulation of electronic property of epitaxial graphene on SiC substrate by Pb intercalation

Manipulating the electronic properties of graphene has been a subject of great interest since it can aid material design to extend the applications of graphene to many different areas. Here, we systematically investigate the effect of lead (Pb) intercalation on the structural and electronic properties of epitaxial graphene on the SiC(0001) substrate. We show that the band structure of Pb-intercalated few-layer graphene can be effectively tuned through changing intercalation conditions, such as coverage, location of Pb, and the initial number of graphene layers. Lead intercalation at the interface between the buffer layer (BL) and the SiC substrate decouples the BL from the substrate and transforms the BL into a p-doped graphene layer. We also show that Pb atoms tend to donate electrons to neighboring layers, leading to an n-doping graphene layer and a small gap in the Dirac cone under a sufficiently high Pb coverage. This paper provides useful guidance for manipulating the electronic properties of graphene layers on the SiC substrate.

36 MATERIALS SCIENCE↗