93 citations · 275 across the 8 of their papers we have counts for
5 papers · 1 filter
An equivariant graph neural network for the elasticity tensors of all seven crystal systems
Mingjian Wen, Matthew K. Horton, Jason M. Munro +2
The elasticity tensor that describes the elastic response of a material to external forces is among the most fundamental properties of materials. The availability of full elasticit…
Bayesian, frequentist, and information geometric approaches to parametric uncertainty quantification of classical empirical interatomic potentials
Yonatan Kurniawan, Cody L. Petrie, Kinamo J. Williams +5
In this paper, we consider the problem of quantifying parametric uncertainty in classical empirical interatomic potentials (IPs) using both Bayesian (Markov Chain Monte Carlo) and…
KLIFF: A framework to develop physics-based and machine learning interatomic potentials
Mingjian Wen, Yaser Afshar, Ryan S. Elliott +1
Interatomic potentials (IPs) are reduced-order models for calculating the potential energy of a system of atoms given their positions in space and species. IPs treat atoms as class…
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…
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…