Domain Knowledge-Driven Machine Learning Model for Performance Prediction and Structural Optimization of Solid Amine CO2 Adsorbents

Solid amine adsorbents have attracted considerable attention for their potential in CO2 capture; however, conventional approaches often fail to fully reflect the comprehensive influence of material properties on adsorption performance. In this study, we propose a feature-enhanced machine learning model (FEXGB-DNN) that integrates domain knowledge to accurately predict the CO2 adsorption capacity and optimize material structure. The model incorporates multidimensional features, including chemical composition, pore structure of the support, and adsorption conditions, and introduces a novel feature termed “amine efficiency”, inspired by feature importance analysis from conventional machine learning models. The FEXGB-DNN model demonstrates excellent generalization and predictive accuracy (RMSE = 0.40, R2 = 0.93, k = 0.91, b = 0.18). Partial dependence plot analysis quantifies the effects of individual features and reveals significant feature interactions, especially in pore structure parameters. Notably, silica-based supports exhibit optimal performance with a common average pore size of approximately 9 nm, a pore volume within 0–0.5 or 2–3 cm3/g, and an organic amine content in the range of 18–20 wt % nitrogen. Furthermore, the FEXGB-DNN model was applied to guide the performance prediction and structural optimization of a specific material, with experimental validation confirming the effectiveness of the model-driven design. This work not only provides a high-precision predictive tool but also, more importantly, reveals the complex structure–property relationships between the physicochemical characteristics of the supports and the final adsorption performance. It thereby establishes both theoretical and practical frameworks for the design of advanced materials for CO2 capture.

固态胺吸附剂因其在二氧化碳（CO₂）捕集领域的应用潜力而受到广泛关注，但传统研究方法往往无法全面反映材料属性对吸附性能的综合影响。本研究提出了一种融合领域知识的特征增强型机器学习模型（FEXGB-DNN），可精准预测二氧化碳吸附容量并优化材料结构。该模型纳入了多维特征，包括载体的化学组成、孔道结构以及吸附工况，并借鉴传统机器学习模型的特征重要性分析思路，引入了名为“胺效率”的新型特征。FEXGB-DNN模型展现出优异的泛化能力与预测精度（均方根误差（Root Mean Square Error, RMSE）=0.40，决定系数（Coefficient of Determination, R²）=0.93，k=0.91，b=0.18）。偏依赖图（Partial dependence plot, PDP）分析量化了单一特征的影响，并揭示了显著的特征交互效应，尤其在孔道结构参数方面表现突出。值得注意的是，硅基载体展现出最优性能，其典型平均孔径约为9 nm，孔容处于0~0.5 cm³/g或2~3 cm³/g区间，有机胺氮含量范围为18~20 wt%。此外，本研究将FEXGB-DNN模型应用于指导特定材料的性能预测与结构优化，经实验验证证实了模型驱动设计的有效性。本研究不仅提供了一种高精度预测工具，更重要的是揭示了载体的物理化学特性与最终吸附性能之间复杂的构效关系，从而为二氧化碳捕集用先进材料的设计构建了理论与实践双重框架。