Wang Chaohong, Pérez de Alba Ortíz Alberto, Dijkstra Marjolein
Soft Condensed Matter & Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, Princetonplein 1, 3584 CC Utrecht, the Netherlands.
Computational Soft Matter, van't Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, the Netherlands.
ACS Nano. 2025 May 13;19(18):17423-17437. doi: 10.1021/acsnano.4c17597. Epub 2025 May 1.
Optimizing the design and synthesis of complex crystal structures presents pivotal opportunities and challenges in materials design. While recent computational advances in inverse design have proven effective for simpler crystals, their extension to intricate structures such as zeolites remains challenging. In this work, we introduce an efficient and robust inverse design workflow specifically tailored for the predictive design of a broad range of complex phases. By integrating an evolutionary parameter optimization strategy with enhanced sampling molecular dynamics simulations, this approach effectively surmounts the high energy barriers that typically hinder self-assembly in these complex structures. We apply this inverse design workflow to facilitate the efficient self-assembly of target zeolite frameworks in an efficient coarse-grained model of a tetrahedral network-forming component and a structure-directing agent. Using this method, we not only successfully reproduce the self-assembly of known structures like the Z1 and SGT zeolites and Type-I clathrates but also uncover previously unknown optimal design parameters for SOD and CFI zeolites. Remarkably, our approach also leads to the discovery of an uncatalogued framework, which we designate as Z5. Our methodology not only enables the screening and optimization of self-assembly protocols but also expands the possibilities for discovering hypothetical structures, driving innovation in materials design and offering a robust tool for advancing crystal engineering in complex systems.
优化复杂晶体结构的设计与合成在材料设计中既带来了关键机遇,也带来了挑战。虽然最近逆设计方面的计算进展已证明对较简单的晶体有效,但将其扩展到诸如沸石等复杂结构仍具有挑战性。在这项工作中,我们引入了一种高效且稳健的逆设计工作流程,该流程专门针对广泛复杂相的预测设计进行了定制。通过将进化参数优化策略与增强采样分子动力学模拟相结合,这种方法有效地克服了通常阻碍这些复杂结构中自组装的高能垒。我们将此逆设计工作流程应用于在四面体网络形成组分和结构导向剂的高效粗粒度模型中促进目标沸石骨架的高效自组装。使用这种方法,我们不仅成功再现了Z1和SGT沸石以及I型笼形包合物等已知结构的自组装,还发现了SOD和CFI沸石以前未知的最佳设计参数。值得注意的是,我们的方法还导致发现了一种未编目的骨架,我们将其命名为Z5。我们的方法不仅能够筛选和优化自组装方案,还扩展了发现假设结构的可能性,推动材料设计创新,并为推进复杂系统中的晶体工程提供了一个强大的工具。