Enabling automated high-throughput Density Functional Theory studies of amorphous material surface reactions

Amorphous materials are increasingly considered for electronic and energy applications. However, modeling site-specific processes on amorphous materials is prohibitively expensive due to the large configuration space, and the large simulation cell needed to properly capture material properties. In this work, we develop a high-throughput workflow, powered by semiempirical methods in combination with Density Functional Theory, to simulate amorphous surfaces and utilize statistical sampling methods to assess surface reaction kinetics. We employ the Smooth Overlap of Atomic Positions (SOAP) to featurize surface species and apply a Bayesian Gaussian Mixture clustering model to perform a site reduction analysis. Finally, we automate the generation of input images for Improved-Tangent Nudged Elastic Band (IT-NEB) to simulate an etching reaction on the reduced sites. The resulting etching barriers are found to follow experimental etching trends for both amorphous silicon and amorphous carbon. Notably, our method reproduces etching barriers using a significantly reduced amount of sites (6 sites) out of all potential sites on the amorphous material surfaces. The successful site reduction method described in this study opens amorphous materials to high-throughput explorations of interfacial chemistry and surface properties.