arXiv subjects

Guoyong Shi

Publications and source records attributed to Guoyong Shi.

3 recordsLinked to original sources

arXiv2025

Temperature-driven structural phase transitions in SmNiO$_3$: insights from deep potential molecular dynamics simulations

The metal-insulator transition (MIT) in rare-earth nickelates exemplifies the intricate coupling between lattice dynamics and electronic effects. This strong interplay makes it challenging to disentangle their individual roles in driving the transition in RNiO3. Here, we isolate the structure response from electronic effect by employing molecular dynamics (MD) simulations based on a machine-learned interatomic potential. Taking SmNiO3 as a prototypical system, our simulations show that the structural phase transition is intrinsically temperature-driven and occurs spontaneously via collective lattice distortions. The simulated critical temperature is 340 K and can be further tuned by pressure. These findings provide atomistic insights into the understanding of structural evolution in triggering the phase transition and hence the MIT in RNiO3.

cond-mat.mtrl-sci↗

arXiv2023

Atomistic simulations of thermodynamic properties of liquid gallium from first principles

In the research of condensed matter, atomistic dynamic simulations play a crucial role, particularly in revealing dynamic processes, phase transitions and thermodynamic statistics macroscopic physical properties in systems such as solids and liquids. For a long time, simulating complex and disordered liquids has been a challenge compared to ordered crystalline structures. The primary reasons for this challenge are the lack of precise force field functions and the neglect of nuclear quantum effects. To overcome these two limits in simulation of liquids, we use a deep potential (DP) with quantum thermal bath (QTB) approach. DP is a machine learning model are sampled from density functional theory and able to do large-scale atomic simulations with its precision. QTB is a method which incorporates nuclear quantum effects by quantum fluctuation dissipation. The application of this first principles approach enable us to successfully describe the phase transition processes in solid and liquid Gallium (Ga) as well as the associated dynamic phenomena. More importantly, we obtain the thermodynamic properties of liquid Ga, such as internal energy, specific heat, enthalpy change, entropy and Gibbs free energy, and these results align remarkably well with experiments. Our research has opened up a new paradigm for the study of dynamics and thermodynamics in liquids, amorphous materials, and other disordered systems, providing valuable insights and references for future investigations.

cond-mat.mtrl-sci↗

arXiv2022

Multilayer Perceptron Based Stress Evolution Analysis under DC Current Stressing for Multi-segment Wires

Electromigration (EM) is one of the major concerns in the reliability analysis of very large scale integration (VLSI) systems due to the continuous technology scaling. Accurately predicting the time-to-failure of integrated circuits (IC) becomes increasingly important for modern IC design. However, traditional methods are often not sufficiently accurate, leading to undesirable over-design especially in advanced technology nodes. In this paper, we propose an approach using multilayer perceptrons (MLP) to compute stress evolution in the interconnect trees during the void nucleation phase. The availability of a customized trial function for neural network training holds the promise of finding dynamic mesh-free stress evolution on complex interconnect trees under time-varying temperatures. Specifically, we formulate a new objective function considering the EM-induced coupled partial differential equations (PDEs), boundary conditions (BCs), and initial conditions to enforce the physics-based constraints in the spatial-temporal domain. The proposed model avoids meshing and reduces temporal iterations compared with conventional numerical approaches like FEM. Numerical results confirm its advantages on accuracy and computational performance.

cs.LG↗