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Eline K. Kempkes

Publications and source records attributed to Eline K. Kempkes.

2 recordsLinked to original sources

Vacancy defects in square-triangle tilings and their implications for quasicrystals formed by square-shoulder particles

Almost all observed square-triangle quasicrystals in soft-matter systems contain a large number of point-like defects, yet the role these defects play in stabilizing the quasicrystal phase remains poorly understood. In this work, we investigate the thermodynamic role of such defects in the widely observed 12-fold symmetric square-triangle quasicrystal. We develop a new Monte Carlo simulation to compute the configurational entropy of square-triangle tilings augmented to contain two types of irregular hexagons as defect tiles. We find that the introduction of defects leads to a notable entropy gain, with each defect contributing considerably more than a conventional vacancy in a periodic crystal. Intriguingly, the entropy gain is not simply due to individual defect types but isamplified by their combinatorial mixing. We then apply our findings to a microscopic model of core-corona particles interacting via a square-shoulder potential. By combining the configurational entropy with vibrational free-energy calculations, we predict the equilibrium defect concentration and confirm that the quasicrystalline phase contains a higher concentration of point-defects than a typical periodic crystal. These results provide a new understanding of the prominence of observed defects in soft-matter quasicrystals.

cond-mat.soft↗

Bayesian umbrella quadrature accelerates free-energy calculations across diverse molecular systems and processes

Biased sampling in molecular dynamics simulations overcomes timescale limitations and delivers free-energy landscapes, essential to understand complex atomistic phenomena. However, when applied across diverse systems and processes, biasing protocols often require time- and resource-consuming fine-tuning. In search for robustness, we boost a prominent biasing method, Umbrella Sampling. To estimate the value of an integral, i.e., the free energy, our Bayesian Umbrella Quadrature (BUQ) method iteratively selects gradient samples, i.e., bias locations, that most reduce the posterior integral variance based on a noise-tolerant Gaussian process model, which also effectively interpolates between samples. We validate the method for a conformational change in a small peptide, a water-to-ice phase transition, and a substitution chemical reaction; obtaining excellent accuracies and speedups. To ease adoption of this more automated and universal free-energy method, we interface BUQ with wide-spread simulation packages and share hyperparametrization guidelines.

cond-mat.stat-mech↗