Michele Ceriotti – Symmetry, locality and long-range interactions in atomistic machine learning

Symmetry, locality and long-range interactions in atomistic machine learning
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Machine learning models are proving to be extremely effective in
predicting the properties of atomistic configurations of matter,
circumventing the need for time consuming electronic structure
calculations. The most succesful schemes achieve transferability
by means of a local representation of structures, in which the
problem of predicting a property is broken down into the prediction
of local, atom-centred contributions, and fundamental symmetries
are incorporated exactly. This approach is however not efficient
in describing long-range interatomic forces, such as those arising
due to electrostatics.
I will present a possible solution to this conundrum based on the
long-distance equivariant (LODE) framework, that combines a local
description of matter with the appropriate, long-range asymptotic
behaviour of interactions. 

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