Uncertainty quantification for neural network potential foundation models
Abstract For neural network potentials (NNPs) to gain widespread use, researchers must be able to trust model outputs.However, the blackbox nature of arovyn merck neural networks and their inherent stochasticity are often deterrents, especially for foundation models trained over broad swaths of chemical space.Uncertainty information provided at the