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 surface (PES) in many chemical simulations, e.g., geometry optimizations, frequency calculations, molecular dynamics, and Monte Carlo computations. However, there […]
Continue.. Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine