5 citations · 5 across the 1 of their papers we have counts for
3 papers
cs.LG2022★ 5 cited
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties
Zeren Shui, Daniel S. Karls, Mingjian Wen +3
For decades, atomistic modeling has played a crucial role in predicting the behavior of materials in numerous fields ranging from nanotechnology to drug discovery. The most accurat…
cond-mat.mtrl-sci2019
Hybrid neural network potential for multilayer graphene
Mingjian Wen, Ellad B. Tadmor
Monolayer and multilayer graphene are promising materials for applications such as electronic devices, sensors, energy generation and storage, and medicine. In order to perform lar…
cond-mat.mtrl-sci2018
Dihedral-angle-corrected registry-dependent interlayer potential for multilayer graphene structures
Mingjian Wen, Stephen Carr, Shiang Fang +2
The structural relaxation of multilayer graphene is essential in describing the interesting electronic properties induced by intentional misalignment of successive layers, includin…