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An adaptive genetic algorithm for crystal structure prediction.
1Department of Physics, Xiamen University, Xiamen 361005, People's Republic of China. Ames Laboratory-US DOE and Department of Physics and Astronomy, Iowa State University, Ames, IA 50011, USA.
Journal of Physics. Condensed Matter : an Institute of Physics Journal
|December 20, 2013
Summary
We developed an efficient genetic algorithm (GA) combining classical potentials and density functional theory (DFT) for materials discovery. This method accelerates the search for complex structures, enabling the study of larger and more intricate systems.
Area of Science:
- Materials Science
- Computational Chemistry
- Solid State Physics
Background:
- Discovering novel materials with desired properties requires efficient structure prediction methods.
- Traditional methods often face limitations in exploring large and complex chemical spaces.
- First-principles calculations, like DFT, offer high accuracy but are computationally expensive for exhaustive searches.
Purpose of the Study:
- To develop a highly efficient genetic algorithm (GA) for accelerated materials structure prediction.
- To combine the speed of classical potentials with the accuracy of DFT for enhanced computational materials discovery.
- To enable the investigation of larger and more complex material systems using first-principles calculations.
Main Methods:
- An adaptive and iterative approach integrating classical potentials for rapid structure exploration.
- Density functional theory (DFT) calculations for accurate energy and structure refinement.
- A genetic algorithm (GA) framework to guide the search process efficiently.
Main Results:
- Achieved several orders of magnitude increase in efficiency for DFT-based GA structural searches.
- Successfully identified complex binary and ternary intermetallic compound structures (36 and 54 atoms/cell).
- Discovered a novel multi-TPa Mg-silicate phase with up to 56 atoms per unit cell.
Conclusions:
- The developed hybrid GA significantly enhances the efficiency of first-principles materials discovery.
- This approach allows for the study of significantly larger and more complex material systems.
- The discovered Mg-silicate phase has potential implications for understanding exoplanetary mantle composition.
