The Robustness of Cluster Expansion: Assessing the Roles of Relaxation and Numerical Error
arXiv:1701.03080 · doi:10.1103/PhysRevB.96.014107
Abstract
Cluster expansion (CE) is effective in modeling the stability of metallic alloys, but sometimes cluster expansions fail. Failures are often attributed to atomic relaxation in the DFT-calculated data, but there is no metric for quantifying the degree of relaxation. Additionally, numerical errors can also be responsible for slow CE convergence. We studied over one hundred different Hamiltonians and identified a heuristic, based on a normalized mean-squared displacement of atomic positions in a crystal, to determine if the effects of relaxation in CE data are too severe to build a reliable CE model. Using this heuristic, CE practitioners can determine a priori whether or not an alloy system can be reliably expanded in the cluster basis. We also examined the error distributions of the fitting data. We find no clear relationship between the type of error distribution and CE prediction ability, but there are clear correlations between CE formalism reliability, model complexity, and the number of significant terms in the model. Our results show that the \emph{size} of the errors is much more important than their distribution.
12 pages + 11 pages in supplementary materials, 5 tables in supplementary materials, 13 main figures, 10 supplementary figures
References in corpus (1)
Cited by in corpus (6)
- Accurate representation of formation energies of crystalline alloys with many components
- SAMPLE: Surface structure search enabled by coarse graining and statistical learning
- A tale of two phase diagrams: Interplay of ordering and hydrogen uptake in Pd-Au-H
- Facilitating {\it ab initio} configurational sampling of multicomponent solids using an on-lattice neural network model and active learning
- Direct coupling of first-principles calculations with replica exchange Monte Carlo sampling of ion disorder in solids
- Free energy of (CoxMn1-x)3O4 mixed phases from machine-learning-enhanced ab initio calculations