Developing Cheap but Useful Machine Learning based Models for Investigating High-Entropy Alloy Catalysts

This work aims to address the challenge of developing interpretable ML-based models when access to large scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor based approaches, machine learning based force fields and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N and NHx (x = 1, 2 and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including, (1) using a sequential optimization protocol, (2) developing a new-geometry based descriptor, and (3) re-purposing the already-available low-cost DFT optimization trajectories to develop a ML-FF. Taken together, this study illustrates how cheap DFT calculations and appropriately designed descriptors can be used to develop cheap but useful models for predicting high-quality adsorption energies at significantly lower computational costs. We anticipate that this resource-efficient philosophy may be broadly relevant to the larger surface catalysis community.