(687d) Deep Learning Boosted Field-Driven Catalysis

External electric fields can modify binding energies of reactive surface species and enhance catalytic performance of heterogeneously catalyzed reactions. Large electric fields can be experimentally generated through three ways: (1) internally over molecular length scales in (metallo-)enzyme and zeolite catalytic active sites; (2) externally in gas/solid heterogeneous catalytic system, such as ultra-high vacuum conditions via scanning tunneling microscopy, field ion/emission microscopy, or flow reactor type via dielectric barrier discharge, coaxial capacitor, and microwave reactor; and (3) in an interfacial way at gas/liquid/solid triple phase boundary.

Compared to experimental studies, theoretical work on electric field effects in catalysis is very limited due to the low efficiency of pure DFT calculations for predicting field-dependent energetics of catalytic reactions. This has led to an incomplete picture of how electric fields influence catalytic mechanisms at the atomic-scale and hinders the design and optimization of field-induced catalytic technologies.

To address this gap, we designed a novel deep learning framework for predicting the field-dependent adsorption energies. Specially, we employed a Graph Neural Network (GNN) to capture the relationship among the geometries, followed by a shared multiple-layer perception (MLP) for field-induced catalytic reaction prediction. The deep learning algorithm developed here accelerates field-dependent energy predictions with acceptable accuracies by five orders of magnitudes compared to DFT alone and has the capacity of transferability, which can predict field-dependent energetics of other catalytic surfaces with high quality performance using little training data (Figure 1).¹ Our designed deep learning framework can provide potential good catalyst candidates for field-induced heterogeneous catalysis in a short time. By this means, some unacceptable catalyst candidates can be quickly filtered out, thus avoiding the unnecessary computations.

Reference

1. M. Wan, H. Yue, J. Notarangelo, H. Liu, F. Che*, “Deep-Learning Assisted Electric Field-Accelerated Ammonia Synthesis”, JACS Au, Accepted, 2022, ACS Editor’s Choice.