A Genetic Algorithm Based Inverse Band Structure Method for Semiconductor Alloys

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

doi:10.1016/j.jcp.2005.03.005

A Genetic Algorithm Based Inverse Band Structure Method for Semiconductor Alloys

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

Computational Science

National Renewable Energy Laboratory

Research output: Contribution to journal › Article › peer-review

37 Scopus Citations

Abstract

We present an efficient and accurate method for searching for atomic configurations with target band structure properties. Our approach to this inverse problem is to search the atomic configuration space by repeatedly applying a forward solver, guiding the search toward the optimal configuration using a genetic algorithm. For the forward solver, we relax the atomic positions, then solve the Schrödinger equation using a fast empirical pseudopotential method. We employ a hierarchical parallelism for the combined forward solver and 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 and discuss the results. This approach can be generalized to a wide range of applications in material design.

Original language	American English
Pages (from-to)	735-760
Number of pages	26
Journal	Journal of Computational Physics
Volume	208
Issue number	2
DOIs	https://doi.org/10.1016/j.jcp.2005.03.005 https://doi.org/10.1016/j.jcp.2005.03.005
State	Published - 20 Sep 2005

NREL Publication Number

NREL/JA-530-38811

Keywords

Electronic structure
Genetic algorithms
Inverse problem
Material design
Optimization
Pseudopotentials

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title = "A Genetic Algorithm Based Inverse Band Structure Method for Semiconductor Alloys",

abstract = "We present an efficient and accurate method for searching for atomic configurations with target band structure properties. Our approach to this inverse problem is to search the atomic configuration space by repeatedly applying a forward solver, guiding the search toward the optimal configuration using a genetic algorithm. For the forward solver, we relax the atomic positions, then solve the Schr{\"o}dinger equation using a fast empirical pseudopotential method. We employ a hierarchical parallelism for the combined forward solver and 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 and discuss the results. This approach can be generalized to a wide range of applications in material design.",

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AU - Kim, Kwiseon

AU - Graf, Peter A.

AU - Jones, Wesley B.

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N2 - We present an efficient and accurate method for searching for atomic configurations with target band structure properties. Our approach to this inverse problem is to search the atomic configuration space by repeatedly applying a forward solver, guiding the search toward the optimal configuration using a genetic algorithm. For the forward solver, we relax the atomic positions, then solve the Schrödinger equation using a fast empirical pseudopotential method. We employ a hierarchical parallelism for the combined forward solver and 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 and discuss the results. This approach can be generalized to a wide range of applications in material design.

AB - We present an efficient and accurate method for searching for atomic configurations with target band structure properties. Our approach to this inverse problem is to search the atomic configuration space by repeatedly applying a forward solver, guiding the search toward the optimal configuration using a genetic algorithm. For the forward solver, we relax the atomic positions, then solve the Schrödinger equation using a fast empirical pseudopotential method. We employ a hierarchical parallelism for the combined forward solver and 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 and discuss the results. This approach can be generalized to a wide range of applications in material design.

KW - Electronic structure

KW - Genetic algorithms

KW - Inverse problem

KW - Material design

KW - Optimization

KW - Pseudopotentials

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A Genetic Algorithm Based Inverse Band Structure Method for Semiconductor Alloys

Abstract

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Keywords

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