解读热力石模酸框架中相变的控制因子
Tao Du1, Shanwu Li2, Sudheer Ganisetti1
1Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark.
National science review
|April 1, 2024
概括
研究人员开发了一种深度学习力场,用于研究极性意达酸框架 (ZIF) 中的相位过渡. 这种方法准确地模拟了ZIF在化和玻璃形成过程中的结构演变,为金属有机框架 (MOF) 玻璃提供了洞察力.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 固态化学 固态化学
背景情况:
- 石化伊米达酸框架 (ZIFs) 呈现复杂的相变,包括化和玻璃化.
- 在ZIFs中无形相转换期间研究结构演变在实验上具有挑战性,特别是在中等范围的长度尺度上.
研究的目的:
- 开发一种计算方法,用于准确模拟ZIF在相位过渡期间的结构演变.
- 引入一种用于预测ZIF眼镜中的结晶倾向的新型描述符.
主要方法:
- 训练基于深度学习的力场,以表征晶体和非晶体ZIF阶段.
- 在ZIF融化和玻璃形成过程中使用训练力场复制结构演变趋势.
- 开发一个"环方位指数"来量化结晶倾向.
主要成果:
- 深度学习力场准确地重现了ZIF结构演变在各种长度尺度上,与ab initio方法相比,但计算成本较低.
- 环方位指数有效地捕捉了ZIF-4玻璃的结晶倾向和ZIF-zni从高密度无形相的形成.
- 晶体形成涉及伊米达环的重定向,导致中心四面体周围的秩序丧失.
结论:
- 开发的深度学习方法为研究 ZIF 阶段过渡提供了一种高效准确的方法.
- 环方位指数是理解和预测无形ZIF晶相形成的有价值工具.
- 这项工作提供了适用于其他金属有机框架 (MOF) 和MOF玻璃设计的相变的见解.
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