MetaMD outperforms other sampling methods in training neural network model chemistries.
problem Improving neural network model chemistries for accurate chemical space exploration.
method Competitive evaluation of molecular dynamics, normal-mode sampling, and Metadynamics for preparing training geometries.
result MetaMD is an efficient and scalable method for training neural network model chemistries.