Kavli Affiliate: Ran Wang | First 5 Authors: Fuchun Ge, Ran Wang, Chen Qu, Peikun Zheng, Apurba Nandi | Summary: Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set […]
Continue.. Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine