Machine Learning Guided Discovery of Non‐Linear Optical Materials

Abstract: Nonlinear optical (NLO) materials are crucial in achieving desired frequencies in solid‐state lasers. So far, new NLO materials have been discovered using high‐throughput calculations or chemical intuition. This study demonstrates the effectiveness of utilizing a high refractive index as a proxy for a high second harmonic generation (SHG) coefficient. It also emphasizes the importance of hardness in screening balanced NLO materials. Two machine learning models are developed to predict refractive indices and Vickers hardness. By applying these models to the OQMD database, potential NLO candidates are identified based on non‐centrosymmetricity, refractive index, hardness value, and bandgap properties. These findings are validated using density functional theory (DFT) calculations. Notably, this approach successfully identifies several already established NLO materials, reinforcing the validity of the methodology.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Machine Learning Guided Discovery of Non‐Linear Optical Materials ; day:27 ; month:11 ; year:2024 ; extent:6 ; Advanced theory and simulations ; (27.11.2024) (gesamt 6)

Contributors, Places and Time

• Creator: Mondal, Sownyak ; Hammad, Raheel

Further information

• DOI: 10.1002/adts.202400463
• URN: urn:nbn:de:101:1-2411271424380.160312066795
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:34 AM CEST