David PfauOK - time for a preprint!
Proud to share another fantastic piece of work from Gino Cassella, extending the FermiNet and neural network VMC to calculate how positrons interact with matter!
arxiv.org/abs/2310.05607
Positrons are the antiparticle to electrons. If you combine them with normal matter they annihilate and create gamma rays, but not before bouncing around and occasionally binding to molecules or filling in gaps in materials.
Positrons are used in all sorts of technology - medical (positron emission tomography), material (positron annihilation spectroscopy) and more exotic proposals, like gamma ray lasers.
It’s very hard to calculate positron binding properties with QMC, especially for non-polar molecules. How hard? Well, this recent Nature paper claimed it was basically impossible for anything larger than miniscule molecules like LiH. But we did it!
https://www.nature.com/articles/s41586-022-04703-3
https://www.nature.com/articles/s41586-022-04703-3
We showed that neural network VMC methods like the FermiNet can be used to accurately calculate binding energies between positrons and molecules for large challenging non-polar molecules like benzene.
The great thing about this is it was an *extremely simple* change to the base FermiNet. This shows part of why neural network VMC is so powerful - it is very easily *extensible* to all sorts of new applications.
