Challenges and Opportunities of Pretrained Machine Learning Interatomic Potentials in Heterogeneous Catalysis

The design of catalysts gets its fundamental rationale from accurate and efficient modeling of reactivity on surfaces and materials. To reach this detailed atomistic understanding, density functional theory (DFT) has been the key computational technique. However, the emergence of machine learning interatomic potentials (MLIPs) marks a significant paradigm shift, offering the potential to match DFT accuracy at a drastically reduced computational cost. This perspective provides an overview of state-of-the-art MLIPs for heterogeneous catalysis as "out-of-the-box" tools. We summarize the different families of MLIPs and their training processes and then apply these pretrained models to heterogeneous catalysis problems. Furthermore, we critically address the challenges of model transferability and integration in unified frameworks, underscoring the necessity for standardized protocols to benchmark performance across different architectures. Finally, we assess the capacity of pretrained models to democratize computational catalysis, highlighting the specific hurdles that remain in achieving reliable, predictive power for widespread use.