Genetic Algorithm-Accelerated Computational Discovery of Liquid Crystal Polymers with Enhanced Optical Properties
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912382904696832 |
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| author | Zhou, Jianing Huang, Yuge Boromand, Arman Noori, Keian Purvis, Lafe Oh, Chulwoo Lu, Lu Ulissi, Zachary W. Gharakhanyan, Vahe Zhang, Xinyue |
| author_facet | Zhou, Jianing Huang, Yuge Boromand, Arman Noori, Keian Purvis, Lafe Oh, Chulwoo Lu, Lu Ulissi, Zachary W. Gharakhanyan, Vahe Zhang, Xinyue |
| contents | Liquid crystal polymers with exceptional optical properties are highly promising for next-generation virtual, augmented, and mixed reality (VR/AR/MR) technologies, serving as high-performance, compact, lightweight, and cost-effective optical components. However, the growing demands for optical transparency and high refractive index in advanced optical devices present a challenge for material discovery. In this study, we develop a novel approach that integrates first-principles calculations with genetic algorithms to accelerate the discovery of liquid crystal polymers with low visible absorption and high refractive index. By iterating within a predefined space of molecular building blocks, our approach rapidly identifies reactive mesogens that meet target specifications. Additionally, it provides valuable insights into the relationships between molecular structure and properties. This strategy not only accelerates material screening but also uncovers key molecular design principles, offering a systematic and scalable alternative to traditional trial-and-error methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13477 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Genetic Algorithm-Accelerated Computational Discovery of Liquid Crystal Polymers with Enhanced Optical Properties Zhou, Jianing Huang, Yuge Boromand, Arman Noori, Keian Purvis, Lafe Oh, Chulwoo Lu, Lu Ulissi, Zachary W. Gharakhanyan, Vahe Zhang, Xinyue Soft Condensed Matter Materials Science Liquid crystal polymers with exceptional optical properties are highly promising for next-generation virtual, augmented, and mixed reality (VR/AR/MR) technologies, serving as high-performance, compact, lightweight, and cost-effective optical components. However, the growing demands for optical transparency and high refractive index in advanced optical devices present a challenge for material discovery. In this study, we develop a novel approach that integrates first-principles calculations with genetic algorithms to accelerate the discovery of liquid crystal polymers with low visible absorption and high refractive index. By iterating within a predefined space of molecular building blocks, our approach rapidly identifies reactive mesogens that meet target specifications. Additionally, it provides valuable insights into the relationships between molecular structure and properties. This strategy not only accelerates material screening but also uncovers key molecular design principles, offering a systematic and scalable alternative to traditional trial-and-error methods. |
| title | Genetic Algorithm-Accelerated Computational Discovery of Liquid Crystal Polymers with Enhanced Optical Properties |
| topic | Soft Condensed Matter Materials Science |
| url | https://arxiv.org/abs/2505.13477 |