activity
20202026
most citedEmergent Mott insulators and non-Hermitian conservation laws in an interacting bosonic chain with noninteger filling and nonreciprocal hopping

21 citations · 33 across the 11 of their papers we have counts for

collaborators

11 papers

cond-mat.stat-mech2026

Emergent Macroscopic Nonreciprocity from Identical Active Particles via Spontaneous Symmetry Breaking

Wei-Chen Guo, Zuo Wang, Pei-Fang Wu +3

Nonreciprocity is known to generate a wide range of exotic phenomena in multi-species many-body systems, where different species influence one another through couplings that violat…

physics.soc-ph2025

Barrier induced stalemate-consensus transition of self-propelled participants subject to majority rule

Yan-Wen Xiao, Wei-Chen Guo, Bao-Quan Ai +1

Natural or artificial barriers, such as the Himalayas, the Berlin Wall, or the Korean Demilitarized Zone, can significantly impede human migration. As a consequence, they may also…

cond-mat.stat-mech2024

Noise-aware neural network for stochastic dynamics simulation

Pei-Fang Wu, Wei-Chen Guo, Liang He

In the presence of system-environment coupling, classical complex systems undergo stochastic dynamics, where rich phenomena can emerge at large spatio-temporal scales. To investiga…

cond-mat.quant-gas2023

Measurement-induced integer families of critical dynamical scaling in quantum many-body systems

Zuo Wang, Shi-Liang Zhu, Li-Jun Lang +1

A quantum many-body system can undergo transitions in the presence of continuous measurement. In this work, we find that a generic class of critical dynamical scaling behavior can…

cond-mat.stat-mech2022

Real-space condensation of reciprocal active particles driven by spontaneous symmetry breaking induced nonreciprocity

Wei-chen Guo, Zuo Wang, Pei-fang Wu +3

We investigate the steady-state and dynamical properties of a reciprocal many-body system consisting of self-propelled active particles with local alignment interactions that exist…

cond-mat.stat-mech2022★ 8 cited

Learning phase transitions from regression uncertainty: A new regression-based machine learning approach for automated detection of phases of matter

Wei-Chen Guo, Liang He

For performing regression tasks involved in various physics problems, enhancing the precision or equivalently reducing the uncertainty of regression results is undoubtedly one of t…

cond-mat.stat-mech2022★ 1 cited

Scaling behavior of transient dynamics of vortex-like states in self-propelled particles

Pei-fang Wu, Wei-chen Guo, Bao-quan Ai +1

Nonequilibrium many-body transient dynamics play an important role in the adaptation of active matter systems environment changes. However, the generic universal behavior of such d…

cond-mat.mes-hall2021★ 21 cited

Emergent Mott insulators and non-Hermitian conservation laws in an interacting bosonic chain with noninteger filling and nonreciprocal hopping

Zuo Wang, Li-Jun Lang, Liang He

We investigate the ground state and quantum dynamics of an interacting bosonic chain with the nonreciprocal hopping. In sharp contrast to its Hermitian counterpart, the ground stat…

cond-mat.soft2021★ 2 cited

Scanning-probe and information-concealing machine learning intermediate hexatic phase and critical scaling of solid-hexatic phase transition in deformable particles

Wei-chen Guo, Bao-quan Ai, Liang He

We investigate the two-dimensional melting of deformable polymeric particles with multi-body interactions described by the Voronoi model. We report machine learning evidence for th…

cond-mat.soft2021

Data-driven criterion for the solid-liquid transition of two-dimensional self-propelled colloidal particles far from equilibrium

Wei-chen Guo, Bao-quan Ai, Liang He

We establish an explicit data-driven criterion for identifying the solid-liquid transition of two-dimensional self-propelled colloidal particles in the far from equilibrium paramet…

cond-mat.dis-nn2020★ 1 cited

Reveal flocking phase transition of self-propelled active particles by machine learning regression uncertainty

Wei-Chen Guo, Bao-Quan Ai, Liang He

We develop the neural network based "learning from regression uncertainty" approach for automated detection of phases of matter in nonequilibrium active systems. Taking the flockin…