AI 中文总结
研究如何通过数据驱动策略设计具有可调负热膨胀的金属有机框架。利用基于MACE-MP-MOF0的高通量工作流程构建数据库，筛选出影响因素，经实验验证设计方案，获得高NTE系数，开启预测性材料设计。
AI 中文摘要
具有负热膨胀（NTE）的材料对于需要精确控制热膨胀的应用至关重要。金属有机框架（MOF）因其特殊的化学可调性、灵活的结构和低能晶格振动，是探索NTE的丰富平台。然而，在庞大的MOF设计空间中揭示控制NTE的结构基序在实验上具有挑战性，大规模第一性原理声子计算在计算上也令人望而却步。本文利用基于MACE-MP-MOF0的高通量工作流程，全面评估影响MOF中NTE的因素，构建了包含12000多种MOF的声子、非弹性中子散射光谱、体积模量和热容量的数据库PhononMOFdb。高通量筛选表明，具有较重、低价金属节点的高度多孔立方拓扑框架有利于强NTE，而连接体功能化提供了在不影响机械稳定性的情况下调节NTE大小和符号的实用方法。通过对Ce-UiO-66 MOF及其溴化变体进行高分辨率温度依赖同步辐射粉末X射线衍射实验验证，证实了设计方案，并获得了超过当前记录的体积NTE系数。这项工作建立了一种数据驱动的策略来设计MOF中的NTE，展示了机器学习加速发现和有针对性的实验验证如何共同开启预测性材料设计。
英文摘要
Materials with negative thermal expansion (NTE) are essential for applications requiring precise control of thermal expansion. Owing to their exceptional chemical tunability, flexible architectures, and low-energy lattice vibrations, metal-organic frameworks (MOFs) represent a rich platform for exploring NTE. However, uncovering the structural motifs that govern NTE across the enormous MOF design space remains experimentally challenging, and large-scale first-principles phonon calculations are computationally prohibitive. Here, we comprehensively evaluate the factors influencing NTE in MOFs by utilizing a high-throughput workflow based on MACE-MP-MOF0, a machine learning interatomic potential fine-tuned for MOFs with near-ab initio accuracy, to construct PhononMOFdb, a database of phonons, inelastic neutron scattering spectra, bulk moduli, and heat capacities for over 12,000 MOFs. High-throughput screening of this database reveals that highly porous cubic topology frameworks with heavier, lower-valent metal nodes favor strong NTE, while linker functionalization provides a practical handle for tuning NTE magnitude and sign without compromising mechanical stability. Experimental validation via high-resolution temperature-dependent synchrotron powder X-ray diffraction on the Ce-UiO-66 MOF and its brominated variants confirms the design recipe and yields volumetric NTE coefficients surpassing current records. This work establishes a data-driven strategy for engineering NTE in MOFs, showing how machine learning-accelerated discovery and targeted experimental validation together unlock predictive materials design.
