通过人工智能镜头看金属有机框架:全面审查
Kevizali Neikha1, Amrit Puzari1
1Department of Chemistry, National Institute of Technology Nagaland, Chumoukedima, Nagaland 797103, India.
Langmuir : the ACS journal of surfaces and colloids
|October 9, 2024
概括
机器学习 (ML) 和人工智能 (AI) 加快了金属有机框架 (MOFs) 合成,结构和属性的预测. 这些计算方法为传统的,资源密集型的方法提供了快速,准确和高效的替代方案,如DFT.
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
- 化学工程是化学工程的重要组成部分.
背景情况:
- 金属有机框架 (MOF) 是先进的多孔材料,在能源和催化中具有重要的应用.
- 由于MOF的复杂性,对MOF的合成和计算分析存在挑战.
- 像密度函数理论 (DFT) 这样的传统方法在计算上昂贵且耗时.
研究的目的:
- 审查机器学习 (ML) 模型在预测MOF合成,结构和属性的应用.
- 探索人工智能 (AI) 与ML的整合,以加强MOF研究.
- 突出ML/AI在加速新型MOF的发现方面的优势.
主要方法:
- 在材料数据集上使用ML技术进行MOF预测.
- 将人工智能模型与机器学习结合起来,用于结构和属性预测.
- 审查在MOF科学中关于ML和AI应用的现有文献.
主要成果:
- 机器学习模型为MOF合成,结构和属性提供快速,高效和准确的预测.
- 人工智能与ML的整合进一步增强了预测能力.
- 这些计算方法显著减少了对大量计算资源的需求.
结论:
- 机器学习和人工智能是MOF研究的转型工具,克服了传统方法的局限性.
- 在MOF科学中采用AI将加速发现具有定制性质的材料.
- 这种方法促进了开发新型的MOFs,用于各种应用,数据输入最小.
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