@inproceedings{01K9ZDQ5KNK5FEQAWCE9P0DQWH,
  abstract     = {{A suitable model for the potential energy surface (PES) is essential to any molecular simulation. Depending on the application of interest and the physics and chemistry at hand, one has to weigh different requirements, such as accuracy, ability to describe relevant processes, and computational efficiency. Machine-learning potentials have reached unprecedented compromises between these requirements: they can be trained to mimic density functional theory (or even better) training data to arbitrary precision, while they are computationally much more efficient than electronic structure methods. Despite their popularity and promise, machine-learning potentials have an important limitation: they are inherently short-ranged. The restriction to short ranges has two origins. First, it is common to use short real-space cutoff spheres to characterize the local environment of an atom. (To some extent, message-passing networks circumvent this limitation: through multiple message-passing iterations, information from beyond the cutoff distance is taken into account.) Second, the number of configurations of atoms at long distances is vast, making it impossible to generate relevant examples for all possibilities. Whenever long-range interactions matter, a physical model is unavoidable. Many physically motivated models for long-range electrostatics and polarization were developed for normal force fields, and were later combined with machine-learning potentials, which handle the short-range interactions. Advantages and disadvantages of several models for long-range interactions will be discussed, and a new framework, called electron machine-learning potential (eMLP), will be presented.}},
  author       = {{Cools-Ceuppens, Maarten and Dambre, Joni and Verstraelen, Toon}},
  booktitle    = {{Twelfth triennial congress of the World Association of Theoretical and Computational Chemists, WATOC 2020}},
  keywords     = {{machine learning,long-range interactions}},
  language     = {{eng}},
  location     = {{Vancouver}},
  title        = {{Physically sound long-range interactions in machine-learning potentials}},
  url          = {{https://www.cheminst.ca/conference/watoc-2020/}},
  year         = {{2022}},
}