Efficient nonparametric n -body force fields from machine learning

By Aldo Glielmo, C. Zeni, A. De Vita. 2018

107 citations recorded by the source corpus.

The authors present a scheme to construct classical $n$-body force fields using Gaussian Process (GP) Regression, appropriately mapped over explicit n-body functions (M-FFs). The procedure is possible, and will yield accurate forces, whenever prior knowledge allows to restrict the interactions to a finite order $n$, so that the ``universal approximator'' resolving power of standard GPs or Neural Networks is not needed. Under these conditions, the proposed construction preserves flexibility of training, systematically improvable accuracy, and a clear framework for validation of the underlying machine learning technique. Moreover, the M-FFs are as fast as classical parametrized potentials, since they avoid lengthy summations over database entries or weight parameters.

Subfields: Electrical and Electronic Engineering, Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

In Materials Chemistry

In Molecular Biology

Citations in the corpus

Physically informed artificial neural networks for atomistic modeling of materials (2018, background): “[48] recently proposed to construct n-body Gaussian process kernels to capture the n-body nature of atomic interactions in physical systems.”
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects (2021, background): “As long as this description is complete, any atom-centered property (including atomic decompositions of extensive properties such as energy) can be predicted from the descriptors.(82) In this context, completeness means that structures which are not convertible into each other (by translations, rota”