The widely-accepted intuition that the important properties of solids are determined by a few key variables underpins many methods in physics. Though this reductionist paradigm is applicable in many physical problems, its utility can be limited because the intuition for identifying the key variables often does not exist or is difficult to develop. Machine learning algorithms (genetic programming, neural networks, Bayesian methods, etc.) attempt to eliminate the a priori need for such intuition but often do so with increased computational burden and human time. A recentlydeveloped technique in the field of signal processing, compressive sensing (CS), provides a simple, general, and efficient way of finding the key descriptive variables. CS is a new paradigm for model building-we show that its models are more physical and predict more accurately than current state-of-the-art approaches, and can be constructed at a fraction of the computational cost and user effort.

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Long-standing challenges in cluster expansion (CE) construction include choosing how to truncate the expansion and which crystal structures to use for training. Compressive sensing (CS), which is emerging as a powerful tool for model construction in physics, provides a mathematically rigorous framework 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 coefficients. Here, we demonstrate the use of BCS to build cluster expansion models for several binary alloy systems. The speed of the method and the accuracy of the resulting fits are shown to be far superior than state-of-the-art evolutionary methods for all alloy systems shown. When combined with high throughput first-principles frameworks, the implications of BCS are that hundreds of lattice models can be automatically constructed, paving the way to high throughput thermodynamic modeling of alloys

Despite the increasing importance of ruthenium in numerous technological applications, e.g. catalysis and electronic devices, experimental and computational data on its binary alloys is sparse. In particular, data is scant on those binary systems believed to be phase separating. We performed a comprehensive study of ruthenium binary systems with the 28 transition metals, using highthroughput first principles calculations. These computations predict novel unsuspected compounds in seven of the 16 binary systems previously believed to be phase separating and in two of the three systems reported with only a high temperature σ-phase. They also predict a few unreported compounds in five additional systems and indicate that some reported compounds may actually be unstable at low temperature. These new compounds may be useful in the rational design of new Ru-based catalysts. The following systems are investigated:

Despite their geometric simplicity, the crystal structures L11 (CuPt) and L13 (CdPt3) do not appear as ground states experimentally, except in Cu-Pt. We investigate the possibility that these phases are ground states in other binary intermetallic systems, but overlooked experimentally. Via the synergy between high throughput and cluster expansion computational methods, we conduct a thorough search for systems that may exhibit these phases and calculate order-disorder transition temperatures when they are predicted. High throughput calculations predict L11 ground states in the following systems: Ag-Pd, Ag-Pt, Cu-Pt, Pd-Pt, Li-Pd, Li-Pt, and L13 ground states in the following systems: Cd-Pt, Cu-Pt, Pd-Pt, Li-Pd, Li-Pt. Cluster expansions confirms the appearance of these ground states in some cases. In the other cases, CE predicts unsuspected derivative superstructures as ground states. The order-disorder transition temperatures for all L11/L13 ground states were found to be sufficiently high that their physical manifestation may be possible.