发表机构
Charter School of Wilmington（威尔明顿特许学校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出集成机器学习框架MatCreatioNN，通过强化学习生成MOF并经CGCNN筛选，发现两种高性能光催化剂，为环境领域高效光催化剂的发现提供了数据驱动方案。
AI 中文摘要
用于环境修复和CO₂转化的光催化剂合理设计，仍受限于多参数光催化行为描述的高计算成本和稀疏实验数据。本研究提出一种集成机器学习框架，将基于强化学习的金属有机框架（MOF）生成与多阶段晶体图卷积神经网络（CGCNN）预测漏斗相结合，以识别在多个电子和结构特征上优化的光催化剂。生成120,000个MOF候选物，通过13个关键描述符筛选，包括带隙适用性、CO₂/H₂O选择性、吸附能和结构稳定性。该漏斗方法将计算成本降低4.13倍，同时保持预测稳健性。两个顶级候选物，Cr基和Zn基MOF，其预测光催化适配值分别比PCN-224(Zr)等基准材料高1.70±0.25倍和1.20±0.05倍，表现出光吸收、氧化还原能学和框架耐久性的同步提升。模拟X射线衍射图谱证实与实验合成MOF的结构高度一致，表明其高可合成性。事后分析揭示了与高预测光催化活性强相关的重复结构基序，如N262金属簇。这些结果凸显了数据驱动方法在加速高效耐用光催化剂发现方面的潜力，为计算设计MOF的实验实现和大规模应用提供了基础。
英文摘要
The rational design of photocatalysts for environmental remediation and CO2 conversion remains limited by the high computational cost and sparse experimental data describing multi-parameter photocatalytic behavior. This work presents an integrated machine-learning framework that couples reinforcement learning-based metal-organic framework (MOF) generation with a multi-stage Crystal Graph Convolutional Neural Network (CGCNN) prediction funnel to identify photocatalysts optimized across multiple electronic and structural features. 120,000 MOF candidates were generated and screened using 13 key descriptors, including band-gap suitability, CO2/H2O selectivity, adsorption energy, and structural stability. The funnel approach reduced computational cost by 4.13-fold while maintaining predictive robustness. Two top candidates, a Cr-based and a Zn-based MOF, exhibited predicted photocatalytic fitness values of 1.70 +/- 0.25 and 1.20 +/- 0.05 fold higher respectively than benchmark materials such as PCN-224(Zr), demonstrating simultaneous improvements in light absorption, redox energetics, and framework durability. Simulated X-ray diffraction patterns confirmed strong structural agreement with experimentally synthesized MOFs, indicating high synthesizability. Post-hoc analysis revealed recurring structural motifs, such as the N262 metal cluster, that correlated strongly with high predicted photocatalytic activity. These results highlight the potential of data-driven methods to accelerate discovery of efficient and durable photocatalysts for environmental and energy-related transformations, providing a foundation for experimental realization and large-scale implementation of computationally designed MOFs.
Comments8 figures, 0 tables. Supplementary information attached to file and data (Zenodo, Github) available
Journal refCatalysis Today, vol. 468, 115725 (2026)
DOI：10.1016/j.cattod.2026.115725
