In-Silico Analysis of High Refractive Index Materials Through Principles of Materials Design

Sadasivan Shankar; Bobby G. Sumpter; Vishnu Shankar; William Goddard; Saber Naserifar

doi:10.1109/MNANO.2024.3475890

In-Silico Analysis of High Refractive Index Materials Through Principles of Materials Design

Sadasivan Shankar, Bobby G. Sumpter, Vishnu Shankar, William Goddard, Saber Naserifar

Center for Nanophase Matls Sciences

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

Abstract

The intent of the paper is to use specific principles of Materials Design that were developed and applied in the electronics industry for enabling understanding and design of improved high refractive index materials. By combining first-principle based ab-initio, semiempirical interatomic potential methods, and machine learning approaches in conjunction with experimental data, we identified specific determinants of high refractive index materials, which can be critically applied for informing materials design and accelerating discovery. Specifically, it was demonstrated that chalcogenides and perovskites as bulk materials can exhibit higher refractive indices with appropriate engineering of specific aspects of the materials.

Original language	English
Pages (from-to)	21-32
Number of pages	12
Journal	IEEE Nanotechnology Magazine
Volume	18
Issue number	6
DOIs	https://doi.org/10.1109/MNANO.2024.3475890
State	Published - 2024

Funding

Material Alchemy, an independent entity for Designing Materials for Sustainability was supported by the U.S. Defense Advanced Research Projects Agency and Triton Systems under Contract FA8650- 20-C-7019. BGS acknowledges support from the Center for Nanophase Materials Sciences, a US Department of Energy Office of Science User Facility operated at Oak Ridge National Laboratory. The work of William Goddard and Bobby G. Sumpter was supported by NSF under Grant CBET 2311117. The project was part of DARPA's efforts to explore the underlying physics behind a materials refractive index within optical frequencies. We appreciate the interactions with many of the project members and discussions with Dr. Baris Unal.

Funders	Funder number
Defense Advanced Research Projects Agency
Oak Ridge National Laboratory
Center for Nanophase Materials Sciences
Office of Science
Triton Systems	FA8650- 20-C-7019
National Science Foundation	CBET 2311117

Keywords

Refractive index
accelerated discovery
atomistic methods
chalcogenides
density functional theory
high refractive index
hybrid methods
in silico
inverse problem
machine learning
materials design
measured properties
perovskites
polarization
quantum methods
semi-empirical
structure-property relations
targeted properties

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10.1109/MNANO.2024.3475890

Cite this

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abstract = "The intent of the paper is to use specific principles of Materials Design that were developed and applied in the electronics industry for enabling understanding and design of improved high refractive index materials. By combining first-principle based ab-initio, semiempirical interatomic potential methods, and machine learning approaches in conjunction with experimental data, we identified specific determinants of high refractive index materials, which can be critically applied for informing materials design and accelerating discovery. Specifically, it was demonstrated that chalcogenides and perovskites as bulk materials can exhibit higher refractive indices with appropriate engineering of specific aspects of the materials.",

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In-Silico Analysis of High Refractive Index Materials Through Principles of Materials Design

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