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This repository contains Python-based tools for analysing molecular dynamics simulations of electrochemical interfaces.
Written by Marko Melander University of Jyväskylä
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Implementation of Adsorbate Chemical-Environment-based Graph Convolution Neural Network (ACE-GCN): framework with the ability to encode atomic configurations comprising of diverse adsorbates, binding locations, coordination environments, and variations in the substrate morphology. This workflow is used to generate and rank surface adsorbate configurations for reactions which are shown to be affected by the presence of high adsorbate surface coverage.
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