混合计算策略用于预测复杂的合金-金属架构
Galymzhan Moldagulov1,2, Kisung Lee1, Sanzhar Nurgaliyev1
1Center for Algorithmic and Robotized Synthesis (CARS), Institute for Basic Science (IBS), Ulsan, Republic of Korea.
Angewandte Chemie (International ed. in English)
|February 21, 2026
概括
本研究介绍了一种使用机器学习 (ML) 的混合计算方法,用于预测金属-连接体协调模式. 在剑桥结构数据库 (CSD) 上训练的ML算法,准确地预测了各种联体和金属的复杂协调.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 化学信息学 化学信息学
背景情况:
- 预测金属-连接物协调对于设计金属复合物和催化剂至关重要.
- 联体可以表现出众多的协调模式,这给化学家带来了挑战.
- 现有的方法与复杂的协调模式作斗争.
研究的目的:
- 开发一种计算方法,用于预测复杂的金属-连接体协调模式.
- 为了创建一个适用于广泛的连接体和金属的多功能模型.
- 为化学家提供一个可访问的工具.
主要方法:
- 一种混合计算方法,结合机器学习 (ML) 和基于知识的规则.
- 在剑桥结构数据库 (CSD) 的数据上训练一个ML算法.
- 开发一个协调模式的预测模型.
主要成果:
- ML模型成功地预测了各种连接体和金属的复杂协调模式.
- 该方法处理各种各样的连接体类型,包括半,触觉和高密度连接体.
- 该模型在不同的金属氧化状态中是有效的.
结论:
- 开发的混合ML方法提供了一个强大的解决方案,用于预测金属-连接体协调.
- 该工具增强了金属复合物和催化剂的合理设计.
- 该算法可通过RDKit和一个公开的网页门户提供.
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