用机器学习潜力转变MOF建模:进展和前景
Omer Tayfuroglu1, Seda Keskin1
1Department of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, Istanbul 34450, Turkey.
Journal of chemical information and modeling
|February 3, 2026
概括
机器学习潜力 (MLP) 为金属有机框架 (MOF) 提供了准确和高效的建模. 为MOF开发通用MLP具有挑战性,但对于材料发现至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 化学物理 化学物理
背景情况:
- 机器学习潜力 (MLP) 将量子力学的精度与仿真效率相结合.
- MLPs模拟复杂的金属有机框架 (MOF) 和它们的客分子相互作用.
- 在灵活的框架中,MLP捕捉了内在的MOF属性和主机-客户行为.
研究的目的:
- 审查基于MLP的MOF建模的当前进展.
- 突出方法,数据生成和主动学习方面的进展.
- 概述普遍的MOF MLPs的挑战和未来方向.
主要方法:
- 从量子力学数据中学习潜在能量表面.
- 模拟MOF属性,如格子动力学和吸附.
- 使用主动学习协议来有效采样数据.
主要成果:
- 机器学习者表现出模拟各种MOF属性和行为的能力.
- 挑战包括MOF多样性,配置采样和MLP实施标准化.
- 在方法学进步和数据策略方面取得了进展.
结论:
- 可转移和可访问的MLP是预测MOF设计的关键.
- 克服当前的挑战将加速MOF的发现和应用.
- 为了更广泛的采用,需要标准化,用户友好的MLP工具.
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