Evolutionary approach for determining first-principles hamiltonians.

Published

Journal Article

Modern condensed-matter theory from first principles is highly successful when applied to materials of given structure-type or restricted unit-cell size. But this approach is limited where large cells or searches over millions of structure types become necessary. To treat these with first-principles accuracy, one 'coarse-grains' the many-particle Schrodinger equation into 'model hamiltonians' whose variables are configurational order parameters (atomic positions, spin and so on), connected by a few 'interaction parameters' obtained from a microscopic theory. But to construct a truly quantitative model hamiltonian, one must know just which types of interaction parameters to use, from possibly 10(6)-10(8) alternative selections. Here we show how genetic algorithms, mimicking biological evolution ('survival of the fittest'), can be used to distil reliable model hamiltonian parameters from a database of first-principles calculations. We demonstrate this for a classic dilemma in solid-state physics, structural inorganic chemistry and metallurgy: how to predict the stable crystal structure of a compound given only its composition. The selection of leading parameters based on a genetic algorithm is general and easily applied to construct any other type of complex model hamiltonian from direct quantum-mechanical results.

Full Text

Duke Authors

Cited Authors

  • Hart, GLW; Blum, V; Walorski, MJ; Zunger, A

Published Date

  • May 2005

Published In

Volume / Issue

  • 4 / 5

Start / End Page

  • 391 - 394

PubMed ID

  • 15834412

Pubmed Central ID

  • 15834412

International Standard Serial Number (ISSN)

  • 1476-1122

Digital Object Identifier (DOI)

  • 10.1038/nmat1374

Language

  • eng