基于物理的机器学习,用于快速选高储存MOF,具有单调性约束
Xuanjie Chen1, Chunjian Pan1, Shaojun Ren2
1College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, P.R. China.
Journal of chemical theory and computation
|May 7, 2025
概括
物理信息神经网络 (PINNs) 提供了一种物理一致的方法来选金属有机框架 (MOFs),以有效地储存. 这种方法成功地确定了满足美国能源部碳中和经济目标的MOF.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 储能 储能 储能 储能 储能 储能
背景情况:
- 是实现碳中和未来的关键替代能源载体.
- 目前的储存方法面临效率和成本方面的挑战.
- 金属有机框架 (MOFs) 显示出由于其高表面积和可鱼性而有望用于储存气.
研究的目的:
- 开发一个物理一致的机器学习模型来选用于储存的MOF.
- 确定符合特定储目标的高容量MOF.
- 克服传统机器学习模型缺乏物理一致性的局限性.
主要方法:
- 开发一个物理信息神经网络 (PINN),结合晶体学属性关系.
- 将单调关系集成到神经网络架构中.
- 使用大法典蒙特卡罗 (GCMC) 模拟来验证高性能MOF.
主要成果:
- 皮恩成功地选了MOF的高储能容量.
- 通过热图分析,鉴定的MOF表现出有意义的结晶学特征.
- 两种实验合成的MOF,LADQEM_CSD17和LADQEM01_CSD17,满足了美国能源部2025年在船上的储存目标.
结论:
- PINNs提供了一种物理上一致且有效的方法,用于在储存中对MOF进行查.
- 已识别的MOF代表了先进能系统的可行候选者.
- 这种方法加快了对高效和成本效益的储存材料的发现.
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