Material Design via Genetic Algorithms for Semiconductor Alloys and Superlattices

Kwiseon Kim; Peter A. Graf; Wesley B. Jones

doi:10.1063/1.1994506

Material Design via Genetic Algorithms for Semiconductor Alloys and Superlattices

Kwiseon Kim, Peter A. Graf, Wesley B. Jones

Computational Science

National Renewable Energy Laboratory

Research output: Contribution to conference › Paper › peer-review

Abstract

We present an efficient and accurate method for designing materials for electronic applications. Our approach is to search an atomic configuration space by repeatedly applying a forward solver, guiding the search toward the optimal configuration using an evolutionary algorithm. We employ a hierarchical parallelism for the combined forward solver and the genetic algorithm. This enables the optimization process to run on many more processors than would otherwise be possible. We have optimized AlGaAs alloys for maximum bandgap and minimum bandgap for several given compositions. When combined with an efficient forward solver, this approach can be generalized to a wide range of applications in material design.

Original language	American English
Pages	1127-1128
Number of pages	2
DOIs	https://doi.org/10.1063/1.1994506
State	Published - 30 Jun 2005
Event	Physics of Semiconductors: 27th International Conference on the Physics of Semiconductors (ICPS-27) - Flagstaff, Arizona Duration: 1 Jul 2004 → 1 Jul 2004

Conference

Conference	Physics of Semiconductors: 27th International Conference on the Physics of Semiconductors (ICPS-27)
City	Flagstaff, Arizona
Period	1/07/04 → 1/07/04

NREL Publication Number

NREL/CP-530-39355

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10.1063/1.1994506

Cite this

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abstract = "We present an efficient and accurate method for designing materials for electronic applications. Our approach is to search an atomic configuration space by repeatedly applying a forward solver, guiding the search toward the optimal configuration using an evolutionary algorithm. We employ a hierarchical parallelism for the combined forward solver and the genetic algorithm. This enables the optimization process to run on many more processors than would otherwise be possible. We have optimized AlGaAs alloys for maximum bandgap and minimum bandgap for several given compositions. When combined with an efficient forward solver, this approach can be generalized to a wide range of applications in material design.",

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AB - We present an efficient and accurate method for designing materials for electronic applications. Our approach is to search an atomic configuration space by repeatedly applying a forward solver, guiding the search toward the optimal configuration using an evolutionary algorithm. We employ a hierarchical parallelism for the combined forward solver and the genetic algorithm. This enables the optimization process to run on many more processors than would otherwise be possible. We have optimized AlGaAs alloys for maximum bandgap and minimum bandgap for several given compositions. When combined with an efficient forward solver, this approach can be generalized to a wide range of applications in material design.

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Material Design via Genetic Algorithms for Semiconductor Alloys and Superlattices

Abstract

Conference

NREL Publication Number

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