[论文解读] Convolutional Neural Networks and Volcano Plots: Screening and Prediction of Two-Dimensional Single-Atom Catalysts

Haoyu Yang, Juanli Zhao+5 more|arXiv (Cornell University)|Feb 6, 2024

Machine Learning in Materials ScienceMaterials Science被引用 3

一句话总结

本研究提出了一种混合框架，将多分支卷积神经网络（CNNs）与火山图结合，用于筛选和预测二维单原子催化剂（SACs）在CO2还原中的性能。通过以态密度（eDOS）作为输入，CNN模型预测的吸附能平均绝对误差为0.1 eV，实现了对CH4生成最优SACs的精确识别，方法基于轨道级屏蔽与位移实验。

ABSTRACT

Single-atom catalysts (SACs) have emerged as frontiers for catalyzing chemical reactions, yet the diverse combinations of active elements and support materials, the nature of coordination environments, elude traditional methodologies in searching optimal SAC systems with superior catalytic performance. Herein, by integrating multi-branch Convolutional Neural Network (CNN) analysis models to hybrid descriptor based activity volcano plot, 2D SAC system composed of diverse metallic single atoms anchored on six type of 2D supports, including graphitic carbon nitride, nitrogen-doped graphene, graphene with dual-vacancy, black phosphorous, boron nitride, and C2N, are screened for efficient CO2RR. Starting from establishing a correlation map between the adsorption energies of intermediates and diverse electronic and elementary descriptors, sole singular descriptor lost magic to predict catalytic activity. Deep learning method utilizing multi-branch CNN model therefore was employed, using 2D electronic density of states as input to predict adsorption energies. Hybrid-descriptor enveloping both C- and O-types of CO2RR intermediates was introduced to construct volcano plots and limiting potential periodic table, aiming for intuitive screening of catalyst candidates for efficient CO2 reduction to CH4. The eDOS occlusion experiments were performed to unravel individual orbital contribution to adsorption energy. To explore the electronic scale principle governing practical engineering catalytic CO2RR activity, orbitalwise eDOS shifting experiments based on CNN model were employed. The study involves examining the adsorption energy and, consequently, catalytic activities while varying supported single atoms. This work offers a tangible framework to inform both theoretical screening and experimental synthesis, thereby paving the way for systematically designing efficient SACs.

研究动机与目标

• 为克服传统描述符方法在筛选CO2还原用单原子催化剂（SACs）时的局限性。
• 解决评估SAC性能时密度泛函理论（DFT）计算的高成本与复杂性问题。
• 开发一种数据驱动、自动化的高效方法，用于识别具有高活性与选择性、可生成CH4的最优SACs。
• 通过可解释人工智能技术（如eDOS屏蔽与位移实验）揭示电子结构对吸附能的贡献。
• 建立系统性框架，指导高效二维SACs的理论筛选与实验合成。

提出的方法

• 采用多分支CNN模型，以二维电子态密度（eDOS）为输入，预测CO2RR中间体的吸附能。
• 将CNN预测结果整合进一种混合描述符火山图，同时包含C型与O型中间体，以提升活性筛选效果。
• 通过不同掩码宽度（11–51）的eDOS屏蔽实验，识别轨道对吸附能的贡献。
• 开展轨道级eDOS位移实验，探究吸附能对能级位移的敏感性，分辨率设为0.005 eV。
• 利用NVIDIA V100 GPU上的KerasTuner进行HyperBand超参数优化，以高精度训练CNN模型。
• 通过与DFT计算的吸附能对比验证模型性能，实现平均绝对误差（MAE）为0.1 eV。

Figure 1: Catalyst performance analysis pipeline integrating volcano plots and CNN. The figure demonstrates the integrated usage of volcano plots for predictive assessment of existing catalysts, and the utility of CNNs to predict and modulate adsorption energies $E_{\text{ads}}$ using eDOS as input.

实验结果

研究问题

• RQ1 基于eDOS训练的深度学习模型能否准确预测二维单原子催化剂中CO2RR中间体的吸附能？
• RQ2 各个电子轨道如何影响SACs中关键反应中间体的吸附能？
• RQ3 对特定轨道能级进行位移在多大程度上影响SACs的预测催化活性？
• RQ4 结合C型与O型中间体的混合火山图能否提升高效CH4生成催化剂的筛选效果？
• RQ5 可解释人工智能技术（如eDOS屏蔽与位移）能否揭示决定SACs性能的底层电子结构规律？

主要发现

• CNN模型在预测九种CO2RR与HER中间体的吸附能时，平均绝对误差（MAE）为0.1 eV，其精度与传统DFT计算相当。
• eDOS屏蔽实验表明，中心金属原子的d轨道以及载体材料的p轨道对吸附能有显著影响，尤其在费米能级附近。
• 轨道级eDOS位移实验显示，d带中心移动0.005 eV可使预测吸附能变化达0.15 eV，凸显催化活性对电子结构调控的高度敏感性。
• 混合火山图成功识别出具有低过电位与高选择性的有前途SAC候选材料，如Ge@g-C3N4与Sn@BN。
• 该框架实现了对电子结构-活性关系的系统性探索，为催化剂设计提供了超越经验描述符方法的可操作洞见。
• 本研究证明，基于eDOS训练的深度学习模型可自主提取复杂的构效关系，无需人工描述符工程。

Figure 2: CNN for electronic density of states. a . Kendall rank correlation coefficient map illustrating the correlation between adsorption energy and other electronic and elementary descriptors. Definitions of the notations are provided in Supplementary Table 15 . b - c . Architectural of ( b ) th