用可解释模型破解铁基催化剂性能预测难题，找到关键影响因素。
Beyond the Black Box: An Interpretable Machine Learning Framework for Predicting Electronic Structure Microdescriptors and Structure-Performance Relationships in Fe-based Catalytic Systems
- 结合SHAP与树模型，解析催化剂微观描述符与性能关系。
- 非线性模型准确率提升至R²=0.61–0.77，显著优于线性方法。
- 识别出晶格稳定性和几何结构是电子能隙主因，适合材料研发者参考。
当前甲烷部分氧化（POM）等高能耗催化系统的设计仍受限于昂贵的试错实验、不可复现的经验直觉以及缺乏将复杂设计空间与性能关联的框架。本文提出一种可解释机器学习框架，融合基于SHAP的特征重要性分析与树集成模型（随机森林、贝叶斯优化的CatBoost），用于表征Fe-沸石及氧化物负载型催化剂。尽管数据有限，该框架通过识别并排序热力学、结构和几何微描述符，解码其对电子能隙的影响，进而决定选择性、活性和稳定性等宏观性能。研究明确表明，晶格稳定性与几何因素是电子能隙（红氧化学活性的关键代理指标）的主要驱动力，而非整体化学计量。非线性模型在预测中达到R²=0.61–0.77，显著优于传统线性基线（R²=0.32）。该流程提供轻量且可迁移的方法论，生成优先级物理特征列表，可用于加速催化剂筛选，并进一步融入微动力学与反应工程模型，构建复杂反应器的数字孪生，支持自主研发实验室中的预测优化。
原文摘要 · Abstract (English)
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.
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