PCTBagging: From inner ensembles to ensembles. A trade-off between discriminating capacity and interpretability

Ibarguren, Igor; Pérez, Jesús M.; Muguerza, Javier; Arbelaitz, Olatz; Yera, Ainhoa

doi:10.1016/j.ins.2021.11.010

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…On the other hand, even for the complexity models that have high model performance, the inverse correlation between model interpretability and performance limits the interpretability of ML. [26,27] Using ensemble tree models, specifically eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), is advantageous. These models have been proven to possess high accuracy to clarify catalytic performance and offer reliable interpretability for the representation of catalytic activity.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning‐Assisted High‐Throughput Screening for Understanding NRR Electrocatalyst Performance Modulation between Active Center and C‐N Coordination

Sun,

Chen,

Guan

et al. 2023

Energy & Environ Materials

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Understanding the correlation between the fundamental descriptors and catalytic performance is meaningful to guide the design of high‐performance electrochemical catalysts. However, exploring key factors that affect catalytic performance in the vast catalyst space remains challenging for people. Herein, to accurately identify the factors that affect the performance of N2 reduction, we apply interpretable machine learning (ML) to analyze high‐throughput screening results, which is also suited to other surface reactions in catalysis. To expound on the paradigm, 33 promising catalysts are screened from 168 carbon‐supported candidates, specifically single‐atom catalysts (SACs) supported by a BC3 monolayer (TM@VB/C‐Nn = 0–3‐BC3) via high‐throughput screening. Subsequently, the hybrid sampling method and XGBoost model are selected to classify eligible and non‐eligible catalysts. Through feature interpretation using Shapley Additive Explanations (SHAP) analysis, two crucial features, that is, the number of valence electrons (Nv) and nitrogen substitution (Nn), are screened out. Combining SHAP analysis and electronic structure calculations, the synergistic effect between an active center with low valence electron numbers and reasonable C‐N coordination (a medium fraction of nitrogen substitution) can exhibit high catalytic performance. Finally, six superior catalysts with a limiting potential lower than −0.4 V are predicted. Our workflow offers a rational approach to obtaining key information on catalytic performance from high‐throughput screening results to design efficient catalysts that can be applied to other materials and reactions.

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Section: Introductionmentioning

confidence: 99%