3 papers
cond-mat.mtrl-sci2026
Dynamically training machine-learning-based force fields for strongly anharmonic materials
Martin Callsen, Tai-Ting Lee, Mei-Yin Chou
Machine learning (ML) force fields have emerged as a powerful tool for computing materials properties at finite temperatures, particularly in regimes where traditional phonon-based…
cond-mat.mes-hall2023
Competing interlayer interactions in twisted monolayer-bilayer graphene: From spontaneous electric polarization to quasi-magic angle
Wei-En Tseng, Mei-Yin Chou
The family of moiré materials provides a powerful platform for tuning interlayer couplings via the twist angle in systems with large spatial periodicity. In trilayer graphene syste…
cond-mat.mes-hall2023
BN-embedded monolayer graphene with tunable electronic and topological properties
Chih-Piao Chuu, Wei-En Tseng, Kuan-Hung Liu +2
Finding an effective and controllable way to create a sizable energy gap in graphene-based systems has been a challenging topic of intensive research. We propose that the hybrid of…