Speaker
Description
A lot of progress has been made in recent years in the development of machine learning (ML) potentials for atomistic simulations [1]. Neural network potentials (NNPs), which have been introduced more than two decades ago [2], are an important class of ML potentials. While the first generation of NNPs has been restricted to small molecules with only a few degrees of freedom, the second generation extended the applicability of ML potentials to high-dimensional systems containing thousands of atoms by constructing the total energy as a sum of environment-dependent atomic energies [3]. Long-range electrostatic interactions can be included in third-generation NNPs employing environment-dependent charges [4], but only recently limitations of this locality approximation could be overcome by the introduction of fourth-generation ML potentials [5], which are able to describe non-local charge transfer using a global charge equilibration step. In this talk an overview about the evolution of NNPs will be given along with typical applications in large-scale atomistic simulations.
[1] J. Behler, J. Chem. Phys. 145 (2016) 170901.
[2] T. B. Blank, S. D. Brown, A. W. Calhoun, and D. J. Doren, J. Chem. Phys. 103
(1995) 4129.
[3] J. Behler and M. Parrinello, Phys. Rev. Lett. 98 (2007) 146401.
[4] N. Artrith, T. Morawietz, J. Behler, Phys. Rev. B 83 (2011) 153101.
[5] T. W. Ko, J. A. Finkler, S. Goedecker, J. Behler, Nature Comm. 12 (2021) 398.
