32 citations · 40 across the 5 of their papers we have counts for
5 papers
Integrating Deep-Learning-Based Magnetic Model and Non-Collinear Spin-Constrained Method: Methodology, Implementation and Application
Daye Zheng, Xingliang Peng, Yike Huang +8
We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic…
Deep Learning Illuminates Spin and Lattice Interaction in Magnetic Materials
Teng Yang, Zefeng Cai, Zhengtao Huang +10
Atomistic simulations hold significant value in clarifying crucial phenomena such as phase transitions and energy transport in materials science. Their success stems from the prese…
Machine-learning interatomic potential for molecular dynamics simulation of ferroelectric KNbO3 perovskite
Hao-Cheng Thong, XiaoYang Wang, Han Wang +3
Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based materials have been recently dem…
Deep Learning Inter-atomic Potential for Thermal and Phonon Behaviour of Silicon Carbide with Quantum Accuracy
Baoqin Fu, Yandong Sun, Linfeng Zhang +2
Silicon carbide (SiC) is an essential material for next generation semiconductors and components for nuclear plants. It's applications are strongly dependent on its thermal conduct…
A Generalizable Machine-learning Potential of Ag-Au Nanoalloys and its Application on Surface Reconstruction, Segregation and Diffusion
Yinan Wang, Xiaoyang Wang, Linfeng Zhang +2
Owing to the excellent catalysis properties of Ag-Au binary nanoalloy, nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, have been under intense investigation. To…