超越黑箱：一种可解释的机器学习框架，用于预测铁基催化系统中电子结构微描述符和结构-性能关系
Beyond the Black Box: An Interpretable Machine Learning Framework for Predicting Electronic Structure Microdescriptors and Structure-Performance Relationships in Fe-based Catalytic Systems
AI总结：
本文提出一种可解释的机器学习框架，通过SHAP分析和树形集成方法，预测铁基催化剂的电子结构微描述符及结构-性能关系，提升了催化剂筛选效率。
摘要：
当前能源密集型应用（如甲烷转化）的催化剂发现和开发流程受限于昂贵的试错实验、不可重复的化学直觉以及缺乏将复杂催化设计空间与性能联系起来的框架。本文提出了一种可解释的机器学习框架，结合基于SHAP的特征重要性分析（可解释人工智能）和基于树的集成（随机森林和贝叶斯优化的CatBoost）来表征铁沸石和氧化物支持的催化剂用于甲烷部分氧化（POM）。尽管数据有限，该框架通过识别和排序影响电子带隙的热力学、结构和几何微描述符，解码复杂的结构-性能关系。该工作明确表明，热力学晶格稳定性和几何因素是电子带隙（红ox反应性关键代理）的主要驱动因素，而非体相化学计量比。非线性模型达到R2为0.61-0.77，显著优于传统线性基线（R2=0.32）。该工作提供了轻量级通用方法和优先级物理特征列表，用于加速催化剂筛选，并可将这些特征整合到微动力学和反应工程模型中，以创建复杂反应器系统的数字孪生，并在自主研发实验室中实现预测优化。
英文摘要：
The current catalyst discovery and development pipeline for energy-intensive applications like methane conversion remains bottlenecked by expensive trial-and-error experimentation, irreproducible chemical intuition, and a lack of frameworks linking complex catalytic design spaces to performance. This work presents an interpretable machine learning framework that integrates SHAP-based feature importance analysis (Explainable AI) with tree-based ensembles (Random Forest and Bayesian-optimized CatBoost) to characterize Fe-zeolite and oxide-supported catalysts for the partial oxidation of methane (POM). Despite limited data, the framework decodes complex structure-performance relationships by identifying and ranking thermodynamic, structural, and geometric microdescriptors that influence the electronic band gap and govern macroscale performance metrics such as selectivity, activity, and stability. This work explicitly demonstrates that thermodynamic lattice stability and geometric factors are the primary drivers of electronic band gap (a critical proxy for redox reactivity) rather than bulk stoichiometry. Non-linear models achieve an R2 of 0.61 - 0.77, significantly outperforming traditional linear baselines (R2 = 0.32). This workflow provides both a light-weight generalizable methodology and a prioritized list of physical features for accelerated catalyst screening - and these features can subsequently be integrated into microkinetic and reaction engineering models to create digital twins of complex reactor systems and to enable predictive optimization in autonomous R&D laboratories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
