Machine-learning tools have taken us closer to understanding electrons and how they behave in chemical interactions, following news that UK-based AI company DeepMind, owned by Google's parent company Alphabet, has created a tool that solves a fundamental problem with how we model chemistry.
The tool, called DeepMind 21, is based on a modelling method called density functional theory (DFT), which relates the location of electrons in a given group of atoms to the total energy the atoms share to determine the chemical and physical properties of a molecule or material. "DFT is a very widely used tool and it's usually very effective, but it has these failures, so tracking down and understanding these failures is important," says DeepMind's Aron Cohen.
One of those failures is an inability to deal with fractional electrons, a theoretical construct in which the charge of an electron is split into multiple particles. Traditional DFT tools can model systems with one or two electrons, but they fail at modelling those with, say, 1.5 electrons, which is important in cases where an electron is shared between more than one atom.
"On the one hand, fractional electrons are fictitious objects, there's no such thing as a fractional electron – electrons are whole by definition," says James Kirkpatrick at DeepMind. "But by fixing these fractional electron problems, we are able to correctly describe chemical systems which usually have got these fundamental errors in their descriptions."
From New Scientist
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