Low-Cost Pt Alloys for Heterogeneous Catalysis Predicted by Density Functional Theory and Active Learning

Pt
is a key high-performing catalyst for important chemical conversions,
such as biomass conversion and water splitting. Limited Pt reserves,
however, demand that we identify more sustainable alternative catalyst
materials for these processes. Here, we combine state-of-the-art graph
neural networks and crystal graph machine learning representations
with active learning to discover new, low-cost Pt alloy catalysts
for biomass reforming and hydrogen evolution reactions. We identify
12 Pt-based alloys which have comparable catalytic activity to that
of the exemplar Pt(111) surface. Notably, Cu3Pt and FeCuPt2 exhibit near identical catalytic performance as that of Pt(111).
These results demonstrate the potential of machine learning for predicting
new catalytic materials without recourse to expensive DFT geometry
optimizations, the current bottleneck impeding high-throughput materials
discovery. We also examine the performance of d-band
theory in elucidating trends in binary and ternary Pt alloys.