A grand canonical genetic algorithm for the prediction of multi-component phase diagrams and testing of empirical potentials

• Published in: Journal of physics. Condensed matter Vol. 25; no. 49; p. 495401
• Main Authors: Tipton, William W, Hennig, Richard G
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 11.12.2013; Institute of Physics
• Subjects: Algorithms; Aluminum - chemistry; Atomic structure; Chemistry, Inorganic - methods; Condensed matter; Copper - chemistry; Cross-disciplinary physics: materials science; rheology; Empirical analysis; Evolution, Molecular; Exact sciences and technology; Genetic algorithms; Materials science; Mathematical models; Phase diagrams; Phase diagrams and microstructures developed by solidification and solid-solid phase transformations; Phase diagrams of metals and alloys; Physics; Quantum Theory; Searching; Software; Thermodynamics; Zirconium; Zirconium - chemistry; Degrees of freedom; Phase diagrams; Evolutionary algorithm; Aluminium; Empirical model; Genetic algorithms; Grand canonical ensemble; Local structure; Zirconium; Hamiltonians; Transition elements; Embedded atom method; Ab initio calculations; Copper; Atomic structure
• ISSN: 0953-8984; 1361-648X
• DOI: 10.1088/0953-8984/25/49/495401

We present an evolutionary algorithm which predicts stable atomic structures and phase diagrams by searching the energy landscape of empirical and ab initio Hamiltonians. Composition and geometrical degrees of freedom may be varied simultaneously. We show that this method utilizes information from favorable local structure at one composition to predict that at others, achieving far greater efficiency of phase diagram prediction than a method which relies on sampling compositions individually. We detail this and a number of other efficiency-improving techniques implemented in the genetic algorithm for structure prediction code that is now publicly available. We test the efficiency of the software by searching the ternary Zr-Cu-Al system using an empirical embedded-atom model potential. In addition to testing the algorithm, we also evaluate the accuracy of the potential itself. We find that the potential stabilizes several correct ternary phases, while a few of the predicted ground states are unphysical. Our results suggest that genetic algorithm searches can be used to improve the methodology of empirical potential design.