activity
20242026
most citedPre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models

9 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

quant-ph2026

Neural Gauge-P Representation for Open Quantum Dynamics of Interacting Bosons

Xiaodong Cao, Zhicheng Zhong

Simulating the nonequilibrium dynamics of interacting open quantum systems remains challenging beyond small system sizes. Quantum phase-space representations provide a scalable app…

cond-mat.str-el2025

Neural network impurity solver for real-frequency dynamical mean-field theory

Fenglin Deng, Yi Lu, Xiaodong Cao +1

We introduce a neural network impurity solver for real-frequency DMFT that employs a multihead cross-attention mechanism to map hybridization functions to spectral functions, condi…

cond-mat.mtrl-sci2025

Mechanism of Anisotropic Crystallization and Phase Transitions under Van der Waals Squeezing

Yuxiang Gao, Zhicheng Zhong

Mechanical confinement strategies, such as van der Waals (vdW) squeezing, have emerged as promising routes for synthesizing non-vdW two-dimensional (2D) layers, surprisingly yieldi…

cond-mat.mtrl-sci2025

T-MSD: An improved method for ionic diffusion coefficient calculation from molecular dynamics

Yuxiang Gao, Xiaodong Cao, Zhicheng Zhong

Ionic conductivity is a critical property of solid ionic conductors, directly influencing the performance of energy storage devices such as batteries. However, accurately calculati…

cond-mat.mtrl-sci2025★ 9 cited

Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models

Ruoyu Wang, Yuxiang Gao, Hongyu Wu +1

Universal force fields generalizable across the periodic table represent a new trend in computational materials science. However, the applications of universal force fields in mate…

cond-mat.str-el2024

Vision Transformer Neural Quantum States for Impurity Models

Xiaodong Cao, Zhicheng Zhong, Yi Lu

Transformer neural networks, known for their ability to recognize complex patterns in high-dimensional data, offer a promising framework for capturing many-body correlations in qua…