Construction of ground-state preserving sparse lattice models for predictive materials simulations

First-principles based cluster expansion models are the dominant approach in ab initio thermodynamics of crystalline mixtures enabling the prediction of phase diagrams and novel ground states. However, despite recent advances, the construction of accurate models still requires a careful and time-consuming manual parameter tuning process for ground-state preservation, since this property is not guaranteed by default. In this paper, we present a systematic and mathematically sound method to obtain cluster expansion models that are guaranteed to preserve the ground states of their reference data. The method builds on the recently introduced compressive sensing paradigm for cluster expansion and employs quadratic programming to impose constraints on the model parameters. The robustness of our methodology is illustrated for two lithium transition metal oxides with relevance for Li-ion battery cathodes, i.e., Li2xFe2(1−x)O2 and Li2xTi2(1−x)O2, for which the construction of cluster expansion models with compressive sensing alone has proven to be challenging. We demonstrate that our method not only guarantees ground-state preservation on the set of reference structures used for the model construction, but also show that out-of-sample ground-state preservation up to relatively large supercell size is achievable through a rapidly converging iterative refinement. This method provides a general tool for building robust, compressed and constrained physical models with predictive power.Materials simulations: Constructing models guaranteed to preserve the ground statesA method has been developed for performing materials simulations without needing to perform manual parameter tuning for the ground-state. First-principles density functional theory calculations are one of the most commonly used tools for computational materials science research but they cannot easily be applied to large structures that contain many thousands of atoms. In such systems, cluster expansion models are often used but they have a problem: manual parameter tuning is required to preserve the ground-state --- important as this usually governs the materials properties. An international team of researchers led by Gerbrand Ceder from Massachusetts Institute of Technology, the University of California Berkeley and Lawrence Berkeley National Laboratory now present a procedure for constructing cluster expansion models that can preserve the ground states without any need for tuning.