Finding stable multi-component materials by combining cluster expansion and crystal structure predictions

Abstract

A desired prerequisite when performing a quantum mechanical calculation is to have an initial idea of the atomic positions within an approximate crystal structure. The atomic positions combined should result in a system located in, or close to, an energy minimum. However, designing low-energy structures may be a challenging task when prior knowledge is scarce, specifically for large multi-component systems where the degrees of freedom are close to infinite. In this paper, we propose a method for identification of low-energy crystal structures within multi-component systems by combining cluster expansion and crystal structure predictions with density-functional theory calculations. Crystal structure prediction searches are applied to the Mo2AlB2 and Sc2AlB2 ternary systems to identify candidate structures, which are subsequently used to explore the quaternary (pseudo-binary) (MoxSc1–x)2AlB2 system through the cluster expansion formalism utilizing the ground-state search approach. Furthermore, we show that utilizing low-energy structures found within the cluster expansion ground-state search as seed structures within crystal structure predictions of (MoxSc1–x)2AlB2 can significantly reduce the computational demands. With this combined approach, we not only correctly identified the recently discovered Mo4/3Sc2/3AlB2i-MAB phase, comprised of in-plane chemical ordering of Mo and Sc and with Al in a Kagomé lattice, but also predict additional low-energy structures at various concentrations. This result demonstrates that combining crystal structure prediction with cluster expansion provides a path for identifying low-energy crystal structures in multi-component systems by employing the strengths from both frameworks.

A Machine-Learning-Assisted Crystalline Structure Prediction Framework To Accelerate Materials Discovery

“…Historically, the discovery of new crystal structures has primarily relied on experimental approaches. In recent years, tremendous advancements in computational materials science have provided an additional way, crystal structure prediction (CSP) for exploring new crystal structures. − …”

Enhancing Supramolecular Assembly in BODIPY Derivatives: Harnessing Halogen Bonding for Cocrystal Design

“…Supramolecular synthesis, a captivating field of study in chemistry, offers a versatile approach to construct complex structures by leveraging the power of non-covalent interactions. − This intriguing technique can be executed using either a single component or a multicomponent system, known as a cocrystal. − When utilizing a single component, researchers meticulously design a molecule with specific functional groups that can autonomously assemble into the desired architecture through non-covalent interactions. On the other hand, cocrystals, composed of two or more molecular components, are held together by non-covalent forces, yielding an opportunity to combine different components and arrange them within a crystal lattice.…”

Bayesian Optimization Acquisition Functions for Accelerated Search of Energy Convex Hull of Multi-Component Alloys

“…The applications of surrogate models, such as configurational cluster expansion [10], have provided accurate approximations of the energy of the system with fewer expensive virtual experiments, which have been applied to study phase transformation, order-disorder states, and crystalline defects in multi-component systems [4,11,5,12,13,14,15,16,17,18,6,7]. Cluster expansion methods model the formation energies of a multi-component system by simply assigning lattice site variables within a parent lattice type, which further provides a parameterized effective Hamiltonian that can be directly coupled with statistical ensembles and Monte Carlo simulations to evaluate the thermodynamic properties [4,5,6,7].…”