29 citations · 31 across the 2 of their papers we have counts for
2 papers
cond-mat.mtrl-sci2021★ 29 cited
AENET-LAMMPS and AENET-TINKER: Interfaces for Accurate and Efficient Molecular Dynamics Simulations with Machine Learning Potentials
Michael S. Chen, Tobias Morawietz, Hideki Mori +2
Machine learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the comp…
cond-mat.mtrl-sci2021★ 2 cited
Artificial neural network molecular mechanics of iron grain boundaries
Yoshinori Shiihara, Ryosuke Kanazawa, Daisuke Matsunaka +4
This study reports grain boundary (GB) energy calculations for 46 symmetric-tilt GBs in alpha-iron using molecular mechanics based on an artificial neural network (ANN) potential a…