GASpy is able to create various catalyst-adsorbate systems and then use DFT to simulate the adsorption energies of these systems. GASpy_regressions analyzes GASpy's results to create surrogate models that can make predictions on DFT calculations that we have not yet performed. This repository, which is meant to be a submodule of GASpy, uses the predictions created by GASpy_regressions to decide (and queue) which simulations that GASpy should perform next.
The main thing that this repository does is here. This script references a pre-determined catalog of adsorption sites that we enumerated with GASpy; each of these sites has an associated adsorption energy prediction that is created by these notebooks. These predictions are then used to determine which of the sites in our catalog to simulate with DFT. We run this script via cron to keep jobs running continuously.
You will need to first install GASpy. Then to use GASpy_feedback, you will need
to make sure that this repository is cloned into your local repository of GASpy
as a submodule. Then run
via Docker, e.g. docker run -v "/local/path/to/GASpy:/home/GASpy" ulissigroup/gaspy_feedback:latest foo.
Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Note that the repository which we reference in this paper is version 0.1 of GASpy_feedback, which can stil be found here.
Current GASpy_feedback version: 0.20
For an up-to-date list of our software dependencies, you can simply check out how we build our docker image here.