Crystal structure prediction using the Minima Hopping method
arXiv:1007.2003 · doi:10.1063/1.3512900
Abstract
A structure prediction method is presented based on the Minima Hopping method. Optimized moves on the configurational enthalpy surface are performed to escape local minima using variable cell shape molecular dynamics by aligning the initial atomic and cell velocities to low curvature directions of the current minimum. The method is applied to both silicon crystals and binary Lennard-Jones mixtures and the results are compared to previous investigations. It is shown that a high success rate is achieved and a reliable prediction of unknown ground state structures is possible.
9 pages, 6 figures, novel approach in structure prediction, submitted to the Journal of Chemical Physics
References in corpus (5)
- How to quantify energy landscapes of solids
- A Periodic Genetic Algorithm with Real-Space Representation for Crystal Structure and Polymorph Prediction
- The performance of Minima Hopping and Evolutionary Algorithms for cluster structure prediction
- A Bell-Evans-Polanyi principle for molecular dynamics trajectories and its implications for global optimization
- Structural Metastability of Endohedral Silicon Fullerenes
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