通过转移学习和跨材料短暂学习来增强多孔材料中的结构-属性关系
Hyunsoo Park1, Yeonghun Kang1, Jihan Kim1
1Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
ACS applied materials & interfaces
|November 20, 2023
概括
一个新的AI模型,PMTransformer,通过从MOF和COF等多种结构中学习来解决多孔材料研究中的数据不平衡问题. 它能够准确地预测财产,并通过有限的数据获得跨材料的洞察力.
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:
- 多孔材料,包括金属有机框架 (MOF),共价有机框架 (COF),多孔聚合物网络 (PPN) 和热石,对于能源和环境应用至关重要.
- 由于MOF与其他多孔材料相比较先进,存在不对称的数据可用性,这阻碍了全面的研究.
- 开发多孔材料的预测模型对于加速发现和应用至关重要.
研究的目的:
- 引入PMTransformer,一个多式联接式变压器模型,以克服在多孔材料研究中的数据不平衡.
- 为了利用跨材料的少量学习来提高模型性能,使用有限的数据.
- 建立跨物质的关系,并预测在不同的多孔材料类别中看不见的特性.
主要方法:
- 在190万个假设多孔材料 (MOF,COF,PNN,热质) 的大型数据集上进行PMTransformer预训练.
- 实施跨学科的少量学习,以改善精细调整,使用有限的示例.
- 使用转移学习,展示对带间隙等属性的预测能力.
主要成果:
- 在预测各种多孔材料特性方面,PMTransformer 实现了最先进的性能.
- 跨学科的少量学习有效地提高了模型微调,即使数据有限.
- 该模型使用MOF数据成功预测了COF带间隙,并建立了材料间的交叉关系.
结论:
- PMTransformer提供了一种强大的解决方案,用于解决在多孔材料研究中的数据不对称性.
- 跨材料学习有助于更深入地理解不同孔隙材料类别之间的关系.
- 这种方法加速了用于各种应用的新型多孔材料的设计和发现.
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