数据驱动的金属有机框架结构的预测
Elizaveta I Yakovenko1, Iurii M Nevolin2, Anatoliy A Chasovskikh3
1MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow 119192, Russia.
Journal of chemical information and modeling
|February 10, 2025
概括
我们开发了一种基于数据的方法,使用神经网络来预测金属有机框架 (MOF) 拓,克服了高通量计算发现的传统方法的局限性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 晶体学 晶体学是指结晶学.
背景情况:
- 晶体结构预测 (CSP) 对于发现新材料至关重要.
- 在金属有机框架 (MOF) 中进行CSP的传统的*ab initio*方法是计算密集的,并且对于高通量选是不可扩展的.
- 需要有效的计算工具来加速MOF发现.
研究的目的:
- 提出一个数据驱动的方法来预测金属有机框架 (MOF) 的净拓.
- 为了实现新的MOF的更快,更可扩展的计算发现.
- 解决高通量MOF结构预测中的*ab initio*技术的局限性.
主要方法:
- 实施粗粒度神经网络来预测底层网络拓.
- 开发一个数据驱动的计算框架,用于MOF发现.
- 应用机器学习模型来分析和预测MOF结构特征.
主要成果:
- 开发的神经网络模型在预测MOF网络拓学方面表现令人满意.
- 通过限制模型的适用性领域,进一步提高了预测准确性.
- 数据驱动的方法显示了加速MOFs的计算选的希望.
结论:
- 使用神经网络的新型数据驱动方法可以有效地预测MOF网络拓.
- 这种方法为计算MOF发现提供了传统的*ab initio*方法的可扩展替代方案.
- 改进的模型为加速识别新MOF材料提供了有价值的工具.
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