# Slab Graph Convolutional Neural Network for Discovery of N2 Electroreduction Catalysts

## TL;DR

This paper introduces SGCNN, a novel machine learning model based on slab graph convolutional neural networks, designed to predict surface adsorption energies for N2 electroreduction catalysts using only elemental properties, thus accelerating catalyst discovery.

## Contribution

The development of SGCNN, a flexible ML model that predicts adsorption energies without ab initio inputs, enabling faster and more accessible catalyst screening for NRR.

## Key findings

- SGCNN achieves a mean absolute error of 0.23 eV in predicting adsorption energies.
- Binary intermetallics with specific d-electron occupations may enhance NRR performance.
- The model leverages elemental properties, avoiding complex ab initio calculations.

## Abstract

The catalyst development for N2 electroreduction reaction (NRR) with low onset potential and high Faradaic efficiency is highly desired, but remains challenging. Machine learning (ML) recently emerged as a complementary tool to accelerate material discovery; however a ML model for NRR has yet to be developed. Here, we develop and report slab-graph convolutional neural network (SGCNN), an accurate and flexible ML model that is applicable to catalytic surface reactions. With the self-accumulated database of 2,699 surface calculations, SGCNN predict binding energies, ranging over 8 eV, of five key adsorbates (*H, *N2, *N2H, *NH, *NH2) related to NRR performance with the mean-absolute-error of only 0.23eV. Unlike previously available models, SGCNN avoids using ab initio level inputs, instead is solely based on elemental properties that are all readily available in Periodic-Table-of-Elements; true accelerations can be realized. t-distributed stochastic neighbor embedding (t-SNE) analysis reveals that binary intermetallics of averaged d-electron occupation between 4 and 5 could potentially lower the onset potential in N2 electroreduction.

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Source: https://tomesphere.com/paper/1812.02949