An adaptive genetic algorithm for crystal structure prediction
- Xiamen Univ., (People's Republic of China); Ames Lab., Ames, IA (United States)
- Ames Lab., Ames, IA (United States)
- Ames Lab., Ames, IA (United States); Univ. of Minnesota, Minneapolis, MN (United States)
- Univ. of Minnesota, Minneapolis, MN (United States)
We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This strategy increases the efficiency of the DFT-based GA by several orders of magnitude. This gain allows a considerable increase in the size and complexity of systems that can be studied by first principles. The performance of the method is illustrated by successful structure identifications of complex binary and ternary intermetallic compounds with 36 and 54 atoms per cell, respectively. The discovery of a multi-TPa Mg-silicate phase with unit cell containing up to 56 atoms is also reported. Such a phase is likely to be an essential component of terrestrial exoplanetary mantles.
- Research Organization:
- Ames Lab., Ames, IA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- AC02-07CH11358
- OSTI ID:
- 1134607
- Report Number(s):
- IS-J 8227
- Journal Information:
- Journal of Physics. Condensed Matter, Vol. 26, Issue 03; ISSN 0953-8984
- Publisher:
- IOP Publishing
- Country of Publication:
- United States
- Language:
- English
Similar Records
Theoretical search for possible Li–Ni–B crystal structures using an adaptive genetic algorithm
Ternary alloy material prediction using genetic algorithm and cluster expansion