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机器学习辅助的预测光热金属网络的预测

Dongqi Fan¹, Xu Chen¹, Shan Wang¹

¹Stomatological Hospital of Chongqing Medical University, Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, P. R. China.

Angewandte Chemie (International ed. in English)

|January 9, 2025

概括

机器学习加速了从金属网络 (MPN) 中发现高性能光热剂 (PTA) 的发现. 这种方法有效地选用于光热疗法 (PTT) 和生物医学应用的材料.

科学领域:

材料科学材料科学材料科学
生物医学工程生物医学工程
计算化学的计算化学

背景情况:

光热疗法 (PTT) 依赖于有效的光热剂 (PTA).
金属网络 (MPN) 是由于其特性而有前途的PTA.
选MPN以获得最佳光热性能是具有挑战性的,因为化学空间广.

研究的目的:

开发一种机器学习 (ML) 模型,用于预测MPN的光热性能.
为了有效地识别PTT和其他生物医学应用的高性能MPN.

主要方法:

构建了80个模块化MPN的光热特性数据库.
雇佣功能工程和模型培训以优化ML预测.
使用极端梯度提升 (XGBoost) 模型进行材料选.

主要成果:

从44438名候选人的虚拟图书馆中识别了1654个高光热MPN.
在预测高性能MPN的实验验证中取得了70%的成功率.
发现了具有光热抗菌应用显著潜力的新型MPN.

结论:

一种基于ML的创新方法使MPN材料能够有效地对PTT进行选.
这种方法为设计先进的PTA提供了坚实的基础.
该研究强调了ML在加速生物医学应用材料发现方面的潜力.

关键词:

机器学习是机器学习.金属网络金属网络光热剂是一种光热剂.光热疗法是一种光热疗法.

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