利用DFT和机器学习互原子势进行Zeolitic Imidazolate框架的分层晶体结构预测
Hierarchical Crystal Structure Prediction of Zeolitic Imidazolate Frameworks Using DFT and Machine-Learned Interatomic Potentials
AI总结：
本文通过高通量探索锌咪唑盐（ZnIm2）的晶体能景观，利用DFT和机器学习互原子势发现24种已知结构及1484种拓扑结构，其中855种为新拓扑结构，并通过实验验证了预测结构的可靠性。
摘要：
晶体结构预测（CSP）正成为金属有机框架（MOFs）计算设计的强大方法。本文利用CSP对锌咪唑盐（ZnIm2）的晶体能景观进行高通量探索。作为最易多形的Zeolitic Imidazolate框架（ZIF）成员，ZnIm2至少有24种已报告的结构和拓扑形式，且新多形体仍被定期发现。借助定制训练的机器学习互原子势（MLIPs），我们对超过300万个随机生成的晶体堆积排列进行了高通量采样，并识别出9609个能量极小值，具有1484种网络拓扑结构，包括855种未报告的拓扑结构。除一个实验报告的结构外，所有其他ZnIm2的实验报告结构均在搜索范围内最终匹配到预测结构，展示了CSP方法在采样实验相关ZIF结构方面的强大能力。最后，通过拓扑分析、密度和孔隙率考虑，我们确定了一组具有未来实验筛选潜力的结构。此外，还展示了如何通过将实验粉末衍射模式与预测结构的模拟模式进行匹配来识别机机械化学合成MOFs的结构。
英文摘要：
Crystal structure prediction (CSP) is emerging as a powerful method for the computational design of metal-organic frameworks (MOFs). In this article we employ CSP to perform high-throughput exploration of the crystal energy landscape of zinc imidazolate (ZnIm2). As the most polymorphic member of the zeolitic imidazolate framework (ZIF) family, ZnIm2 has at least 24 reported structural and topological forms, and new polymorphs still being regularly discovered. With the aid of custom-trained machine-learned interatomic potentials (MLIPs) we have performed a high-throughput sampling of over 3 million randomly-generated crystal packing arrangements and identified 9609 energy minima characterized by 1484 network topologies, including 855 topologies that have not been reported before. All but one experimentally-reported structures of ZnIm2, falling within the search boundaries, were ultimately matched with the predicted structures, demonstrating the power of the CSP method in sampling experimentally-relevant ZIF structures. Finally, through a combination of topological analysis, density and porosity considerations, we have identified a set of structures representing promising targets for future experimental screening. as well as demonstrated how structures of mechanochemically-synthesized MOFs could be identified via matching experimental powder diffraction patterns with simulated patterns from the predicted structures.
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