Scholarlink

<p>Current investigation presents the design and analysis of 299 azobenzene photoswitches (PSs) for their lowest possible π→ π* transition energies along with their predicted emission maxima values through machine learning (ML) analysis. Their π→ π* transitions related wavelength is calculated by Erying equation to reveal its range up to 256&#xa0;nm. Their Synthetic Accessibility Likelihood Index (SALI) indicates that a substantial number of them can be synthesized with ease. Among various tested ML model, eXtream Gradient Boosting (XGBoost) regression models demonstrates its high accuracy by achieving an R² value of 0.87. Their designed molecular descriptors show its Maximum Electrotopological State Index (MaxEStateIndex) to impact the model most. For its emission wavelengths, the random forest regression model yields its promising results with its R<sup>2</sup> of 0.92 and a Mean Squared Error (MSE) of 0.38. Its SHAP value reveals the top contributing descriptors being Estate_VSA5, NumValenceElectrons, Estate_VSA3, Chi0n, Chi1v, PEOE_VSA9, Chi0v, and VSA_Estate2. This work not only expands the library of azobenzene PSs but also enhances their understanding of their electronic properties for their future applications in materials science.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring New Azobenzene Type Photoswitches by Machine Learning with Lowest Possible Fluorescence Excitons with Ease of Synthesis

  • 期刊论文

  • 发表日期：

    2025年5月6日

查看全文
  • Azal S. Waheeb,
  • Duha M. Hasan,
  • Shaimaa H. Mallah,
  • Sajjad H. Sumrra,
  • Sadaf Noreen,
  • Ashraf Y. Elnaggar,
  • Abrar U. Hassan,
  • Islam H. El Azab,
  • Hussein A. K. Kyhoiesh,
  • Mohamed H. H. Mahmoud

摘要

Current investigation presents the design and analysis of 299 azobenzene photoswitches (PSs) for their lowest possible π→ π* transition energies along with their predicted emission maxima values through machine learning (ML) analysis. Their π→ π* transitions related wavelength is calculated by Erying equation to reveal its range up to 256 nm. Their Synthetic Accessibility Likelihood Index (SALI) indicates that a substantial number of them can be synthesized with ease. Among various tested ML model, eXtream Gradient Boosting (XGBoost) regression models demonstrates its high accuracy by achieving an R² value of 0.87. Their designed molecular descriptors show its Maximum Electrotopological State Index (MaxEStateIndex) to impact the model most. For its emission wavelengths, the random forest regression model yields its promising results with its R2 of 0.92 and a Mean Squared Error (MSE) of 0.38. Its SHAP value reveals the top contributing descriptors being Estate_VSA5, NumValenceElectrons, Estate_VSA3, Chi0n, Chi1v, PEOE_VSA9, Chi0v, and VSA_Estate2. This work not only expands the library of azobenzene PSs but also enhances their understanding of their electronic properties for their future applications in materials science.