Investigation of global charge distributions for constructing non-local machine learning potentials

Machine learning potentials (MLPs) play a vital role in atomistic simulations due to their nearly ab-initio accuracy and their high computational efficiency similar to empirical force fields. However, most MLPs depend on local atomic environments only without taking global charge distributions into account thus neglecting non-local effects. Recently, some non-local MLPs have been proposed [1-3] for tackling these problems. They are not only based on local environments but also on non-local electronic information, i.e. atomic charges depending on long-range charge transfer and the global charge. Here we use fourth-generation high-dimensional neural network potentials [3] to illustrate the role of non-local effects and suggest possible improvements for current state-of-the-art MLPs.
References:
1: Xie, X.; Persson, K. A.; Small, D. W. J. Chem. Theory Comput. 2020, 16, 4256–4270
2: Zubatyuk, R.; Smith, J. S.; Leszczynski, J.; Isayev, O. Sci. Adv. 2019, 5, eaav6490
3: Ko, T. W.; Finkler, J. A.; Goedecker, S.; Behler, J. arXiv:2009.064842020