Obtaining effective extreme ultraviolet lithography (EUVL) materials for pragmatic applications remains challenging. The experimental design and conventional theoretical prediction are time-consuming and costly and hardly affordable to accelerate the discovery of commercial EUVL materials. In this work, we employed the machine learning (ML) technique to predict the ionization potential of promising EUVL materials, which is closely related to the photoresists’ solubility switch. The developed ML model presents a strong generalization ability and can predict new EUVL materials containing different metals (i.e., Mg, Cu, Ca, Cd, Ni). Furthermore, feature analysis indicates that the number of hydrogen bond donors in a compound plays a vital role in determining the ionization potential of EUVL materials. The work provides not only an effective ML model to predict EUVL materials but also crucial insights into the correlation between the structure and properties. Finally, the developed ML model has been integrated into an online platform (https://zinc-oxo-cluster-predictor.streamlit.app/), allowing users to quickly evaluate their designed materials and develop a comprehensive scheme for discovering promising EUVL compounds based on our platform.

中文翻译：

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使用机器学习进行金属核心特异性筛选：加速发现金属氧化物簇以提高 EUV 光刻分辨率


获得有效的极紫外光刻 （EUVL） 材料用于实用应用仍然具有挑战性。实验设计和常规理论预测耗时、昂贵且难以承受，无法加速商业 EUVL 材料的发现。在这项工作中，我们采用机器学习 （ML） 技术来预测有前途的 EUVL 材料的电离电位，这与光刻胶的溶解度开关密切相关。开发的 ML 模型具有很强的泛化能力，可以预测含有不同金属（即 Mg、Cu、Ca、Cd、Ni）的新 EUVL 材料。此外，特征分析表明，化合物中氢键供体的数量在决定 EUVL 材料的电离电位方面起着至关重要的作用。这项工作不仅提供了一个有效的 ML 模型来预测 EUVL 材料，而且还提供了对结构和特性之间相关性的重要见解。最后，开发的 ML 模型已集成到在线平台 （https://zinc-oxo-cluster-predictor.streamlit.app/） 中，使用户能够快速评估他们设计的材料，并基于我们的平台开发一个全面的方案来发现有前途的 EUVL 化合物。