Neural Networks for Modeling Materials with Long-Range Interactions

Neural network interatomic potentials (NNIPs) trained using first principles data have been shown to reach an accuracy close to the reference one at a fraction of the computational cost for relatively simple, well-known systems. Before adapting NNIPs as the workhorse for linear-scaling atomistic simulations however, we need an analysis of the true predictive power and the cost effectiveness of the NNIPs for materials with complex structural and chemical features.<br/> <br/>In this talk I will address this need by first reviewing recent methodological advances in NNIP generation for materials with long range interactions, and then by demonstrating the data-dependence of predictive power on systems ranging from layered materials with dispersion forces to ionic liquids and charged molecules. Finally, I will provide demonstrations of possible paths to increasing cost-effectiveness of NNIPs through techniques developed in the neural network community outside physical sciences, such as transfer learning and pruning.