寻找用于储存的金属有机框架,使用经典和量子主动学习
Maicon Pierre Lourenço1, Rishabh Shukla2, Mosayeb Naseri2,3
1Departamento de Química e Física - Centro de Ciências Exatas, Naturais e da Saúde - CCENS - Universidade Federal do Espírito Santo, 29500-000 Alegre, Espírito Santo, Brazil. maiconpl01@gmail.com.
Physical chemistry chemical physics : PCCP
|October 21, 2025
概括
人工智能,包括量子主动学习 (QAL),可以有效地发现金属有机框架 (MOF) 以最小的数据来进行最佳的储存. 这些方法确定了增强气体吸附的有希望的MOF和实验条件.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 人工智能的人工智能
背景情况:
- 金属有机框架 (MOF) 是先进的多孔材料,具有大量气体储存和分离的潜力.
- 高效的储存对于清洁能源应用至关重要,MOFs提供了一个有前途的途径.
- 发现最佳的MOF和用于储存的实验条件是具有挑战性的,因为设计空间广.
研究的目的:
- 开发和评估主动学习 (AL) 和量子主动学习 (QAL) 方法,用于设计具有增强存储能力的MOF.
- 探索AL和QAL在识别最佳MOF和实验参数 (温度,压力) 的性能,使用文献衍生数据集.
- 评估这些人工智能技术在有限的实验数据下有效地导航MOF设计空间的能力.
主要方法:
- 实施AL和QAL方法用于MOF发现.
- 利用回归模型,包括人工神经网络,支持向量回归和高斯过程 (GP和QGP).
- 采用了各种不确定性量化和获取功能来选择下一个实验目标; QAL特别使用了带有预测量子内核的QGP.
主要成果:
- 通过AL和QAL方法,使用有限的数据集,成功识别了具有增强存储特性的MOF.
- 人工智能方法证明了能够从类似的候选人中区分最佳的MOF和条件.
- 开发了一种网络图形方法来分析AL和QAL在MOF搜索中的表现.
结论:
- 主动学习和量子主动学习是加速发现具有优越储能能力的MOF的有效工具.
- 这些人工智能驱动的方法可以显著减少识别新材料和最佳条件所需的实验力度.
- 该研究强调了古典和量子计算方法在推进能源应用中的材料设计方面的潜力.
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