DAE

Abstract

Environmental and economic industry relies on high-performance materials such as lightweight alloys, recyclable motor vehicle and building components, and high efficiency lighting. Material properties as expressed through crystal structure is crucial to this understanding. Based on first-principles calculations, it is still impossible in most materials to infer ground-state properties purely from a knowledge of their atomic components. Many methods attempt to predict crystal structures and compound stability, we explore models which infer the existence of structures on the basis of combinatorics and geometric simplicity. Computational models based on these first physics principles are called VASP codes. We illustrate the use of a statistical surrogate model to produce predictions of VASP codes as a function of a moderate number of VASP inputs.