Abstract

The steady march of new technology depends crucially on our ability to discover and design new, advanced materials. Partially due to increases in computing power, computational methods are now having an increased role in this discovery process. Advances in this area speed the discovery and development of advanced materials by guiding experimental work down fruitful paths. Density functional theory (DFT)has proven to be a highly accurate tool for computing material properties. However, due to its computational cost and complexity, DFT is unsuited to performing exhaustive searches over many candidate materials or for extracting thermodynamic information. To perform these types of searches requires that we construct a fast, yet accurate model. One model commonly used in materials science is the cluster expansion, which can compute the energy, or another relevant physical property, of millions of derivative superstructures quickly and accurately. This model has been used in materials research for many years with great success. Currently the construction of a cluster expansion model presents several noteworthy challenges. While these challenges have obviously not prevented the method from being useful, addressing them will result in a big payoff in speed and accuracy. Two of the most glaring challenges encountered when constructing a cluster expansion model include:(i) determining which of the infinite number of clusters to include in the expansion, and (ii) deciding which atomic configurations to use for training data. Compressive sensing, a recently-developed technique in the signal processing community, is uniquely suited to address both of these challenges. Compressive sensing (CS) allows essentially all possible basis (cluster) functions to be included in the analysis and offers a specific recipe for choosing atomic configurations to be used for training data. We show that cluster expansion models constructed using CS predict more accurately than current state-of-the art methods, require little user intervention during the construction process, and are orders-of-magnitude faster than current methods. A Bayesian implementation of CS is found to be even faster than the typical constrained optimization approach, is free of any user-optimized parameters, and naturally produces error bars on the predictions made. The speed and hands-off nature of Bayesian compressive sensing (BCS) makes it a valuable tool for automatically constructing models for many different materials. Combining BCS with high-throughput data sets of binary alloy data, we automatically construct CE models for all binary alloy systems. This work represents a major stride in materials science and advanced materials development.

Degree

PhD

College and Department

Physical and Mathematical Sciences; Physics and Astronomy

Rights

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