Optimising computational efficiency for flat-histogram Monte Carlo sampling for high-entropy alloy thermodynamics
Date:
Contributed talk at the 2025 Computing Insight Uk (CIUK) conference.
Abstract
Flat-histogram methods such as Wang Landau sampling [1] provide a means for high throughput calculation of phase diagrams for atomistic/lattice model systems. Many parallelisation schemes have been proposed to accelerate sampling simulations with varying degrees of complexity [2]. In this study, these different schemes are benchmarked - both in isolation and in combination - to establish best practice. The schemes studied include energy domain decomposition with both static sub-domains and a dynamic sub-domain sizing which we propose. We also assess the benefit of replica exchange and including multiple random walkers per sub-domain to determine which factors most significantly improve parallel efficiency and therefore reduce computational costs. The influence of sub-domain overlap will also be discussed. As an illustrative test case, we implement [3] and apply [4] the aforementioned strategies to a lattice-based model describing the internal energies of the AlTiCrMo refractory high-entropy superalloy, which is understood to crystallographically order into a B2 (CsCl) structure with decreasing temperature.
References
[1] F. Wang, D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001).
[2] T. Vogel et al., Phys. Rev. Lett. 110, 210603 (2013).
[3] H. J. Naguszewski et al., arXiv:2505.05393.
[4] C. D. Woodgate, H. J. Naguszewski et al., J. Phys.: Mater. 8, 045002 (2025).