孔隙有机晶体的结构预测
Musiha Mahfuza Mukta1, Romain Perriot2, Shinnosuke Hattori3
1Department of Mechanical Engineering and Engineering Science Charlotte NC USA qzhu8@charlotte.edu.
RSC advances
|February 6, 2026
概括
自动化的晶体结构预测可以重现转移稳定的多孔多态体. 先进的机器学习力场使得新型多孔材料在合成之前能够进行计算设计.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 晶体学 晶体学是指结晶学.
背景情况:
- 多孔材料对于气体储存和催化等应用至关重要.
- 对转移稳定的多孔多态体的实验合成具有挑战性.
- 自动化晶体结构预测 (ACSP) 提供了一个潜在的解决方案.
研究的目的:
- 评估ACSP在复制实验已知的转移性稳定多孔多态体方面的能力.
- 调查不同能源模型对预测准确性的影响.
- 探索新的多孔材料的计算设计的潜力.
主要方法:
- 使用了高通量有机晶体结构预测 (HT-OCSP) 框架.
- 研究了五个代表性的有机晶体系统,包括键框架 (HOF).
- 采用了各种能源模型:经典,机器学习力场,紧密结合和密度函数理论 (DFT).
主要成果:
- 在优化的对称条件下,HT-OCSP框架成功地为多孔材料生成了复杂的转移稳定的晶体候选物.
- 对能量与密度的分析表明,实验结构可以被确定为能量有利的.
- 集成先进的通用机器学习力场显著提高了预测准确度.
结论:
- ACSP是一种可行的方法,用于复制实验观察到的转移稳定的多孔多态体.
- 孔隙材料的计算设计在实验合成之前是可行的,以能量密度分析为指导.
- 开发的框架和机器学习力场加速了新型多孔材料的发现.
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