Cluster expansion made easy with Bayesian compressive sensing
Department of Physics and Astronomy, Brigham Young University, Provo, Utah 84602, USA(Dated: July 10, 2013)Long-standing challenges in cluster expansion (CE) construction include choosing how to truncatethe expansion and which crystal structures to use for training. Compressive sensing (CS), which isemerging as a powerful tool for model construction in physics, provides a mathematically rigorousframework for addressing these challenges. A recently-developed Bayesian implementation of CS(BCS) provides a parameterless framework, a vast speed up over current CE construction techniques,and error estimates on model coecients. Here, we demonstrate the use of BCS to build clusterexpansion models for several binary alloy systems. The speed of the method and the accuracy of theresulting ts are shown to be far superior than state-of-the-art evolutionary methods for all alloysystems shown. When combined with high throughput rst-principles frameworks, the implicationsof BCS are that hundreds of lattice models can be automatically constructed, paving the way tohigh throughput thermodynamic modeling of alloys.I. INTRODUCTION