Zhao Chengxi, Liu Honglai, Qu Da-Hui, Cooper Andrew I, Chen Linjiang
Key Laboratory for Advanced Materials, Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology Shanghai China.
Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory, Department of Chemistry, University of Liverpool Liverpool UK
Chem Sci. 2024 Dec 31;16(5):2363-2372. doi: 10.1039/d4sc06467d. eCollection 2025 Jan 29.
The packing of organic molecular crystals is often dominated by weak non-covalent interactions, making their rearrangement under external stimuli challenging to understand. We investigate a pressure-induced single-crystal-to-single-crystal (SCSC) transformation between two polymorphs of 2,4,5-triiodo-1-imidazole using machine learning potentials. This process involves the rearrangement of halogen and hydrogen bonds combined with proton transfer within a complex solid-state system. We developed a strategy to progressively approach the transition state along the phase transition path from both ends by using both the α and β crystal phases as initial structures for active learning. This method allowed us to develop a DFT-based machine learning potential that faithfully describes both of the stable phases and the transition processes. Our results demonstrate that these anisotropic interactions are represented accurately during molecular dynamic simulations. Bond breaking and reforming during proton transfer is observed and analysed in detail. This approach holds promise for simulating SCSC transitions in organic molecular crystals involving anisotropic interactions and chemical bond changes.
有机分子晶体的堆积通常由弱非共价相互作用主导，这使得它们在外部刺激下的重排难以理解。我们使用机器学习势研究了2,4,5-三碘-1-咪唑两种多晶型物之间的压力诱导单晶到单晶（SCSC）转变。这个过程涉及到复杂固态系统中卤素键和氢键的重排以及质子转移。我们开发了一种策略，通过使用α和β晶相作为主动学习的初始结构，沿着相变路径从两端逐步接近过渡态。这种方法使我们能够开发出一种基于密度泛函理论（DFT）的机器学习势，它能忠实地描述两个稳定相和过渡过程。我们的结果表明，在分子动力学模拟中，这些各向异性相互作用得到了准确的体现。详细观察和分析了质子转移过程中的键断裂和重新形成。这种方法有望用于模拟涉及各向异性相互作用和化学键变化的有机分子晶体中的SCSC转变。
