Name: Accelerated Computational Electrochemical-systems Discovery
Type: Organization
Bio: This is the GitHub organization for the ARPA-E DIFFERENTIATE award with Carnegie Mellon University (lead), Julia Computing, Citrine Informatics, MIT.
Blog: https://www.cmu.edu/aced/
Accelerated Computational Electrochemical-systems Discovery's Projects
Tools for automated structure generation of catalyst systems
Data and scripts in support of the publication "By how much can closed-loop frameworks accelerate computational materials discovery?"
Uncertainty quantification for Deep Learning models in Julia.
Python library to generate input files for DFT codes.
Simple parsers for DFT codes
Comparing Flux 11 vs. 12 on example 1 from AtomicGraphNets
Geometric Deep Learning for Flux
Generalized MHC kinetics for electrochemical interfaces.
Tools for converting from DFT codes into PIF objects
a template for research group sites
The amazing Reaction Mechanism Simulator for simulating large chemical kinetic mechanisms
Julia Project website