Interpretable Attention-based Transfer Learning in Plasma Catalysis: A Study on the Role of Surface Charge

Abstract

Low-temperature plasma catalysis holds promise for electrification of energy-intensive chemical processes such as methane reforming and ammonia synthesis. However, fundamental understanding of plasma-catalyst interactions, essential for catalyst design and screening for plasma catalysts, remains largely limited. Recent work has demonstrated the importance of first-principles studies, including density functional theory (DFT), for elucidating the role of electro- and photo-effects such as electric field and charge in plasma catalysis. The availability of increasing amounts of DFT data in thermal catalysis presents a unique opportunity for plasma catalysis research to efficiently leverage this existing first-principles knowledge of thermal catalysis towards investigating plasma-catalyst interactions. To this end, this paper investigates interpretable transfer learning from thermal to plasma catalysis, with a focus on the role of surface charge. Pre-trained attention-based graph neural networks (GNNs) from the Open Catalysis Project, trained using millions of thermal catalysis DFT data points, are structurally adapted to account for surface charge effects and fine-tuned using plasma catalysis DFT data of single metal atoms on Al$_2$O$_3$ support and adsorbates involved in plasma-catalytic ammonia synthesis. Not only the fine-tuned attention-based GNN model provides a high test accuracy for predicting adsorption energies and atomic forces in plasma catalysis, but also shows adequate extrapolation for unseen single metal atoms in the plasma catalysis data used for the model fine-tuning. To distinguish the effects of surface charge from other dissimilarities in DFT data of thermal and plasma catalysis, a dual-model framework is presented that relies on two pre-trained GNNs, one of which is specifically tasked to capture surface charge effects using an attention mechanism that provides interpretable insights into their role. Lastly, it is demonstrated how the attention-based GNNs developed for single metal atoms can be efficiently adapted for predicting adsorption energies and atomic forces for metal clusters in plasma catalysis. This work highlights the vast potential of interpretable transfer learning from thermal catalysis to plasma catalysis to mitigate excessive computational requirements of first-principles studies in plasma catalysis, towards accelerating fundamental research in this domain.