2026-10-12T01:35:16Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34018269 2026-09-28T19:06:40Z category_4 category_21 category_873 category_734 portal_63 item_type_6 month_year_09_2026

High-Throughput Screening of MN₄ Coordinated Macrocyclic Frameworks for CO Reduction by Combining First-Principles Calculations and Machine Learning Ziyue Zhang (10993713) Mengmeng Xu (1696420) Juan Xie (461923) Yujin Ji (4430380) Huilong Dong (1545064) Youyong Li (599854) Biochemistry Biotechnology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified low limiting potential large configurational space evaluating thermodynamic stability coordinated macrocyclic frameworks complex reaction network accelerate catalyst evaluation multilevel screening strategy guided screening results relatively low δg 8 </ sub 4 </ sub 2 </ sub 1 </ sub full reaction pathway deep co reduction throughput screening pathway calculations co reduction test sets subsequently carried rational discovery promising route principles calculations ml algorithm ml ). machine learning key step final product examined candidates dissolution resistance coordination centers combining first chose two added c 90 candidates 30 v The electrochemical CO reduction reaction (CORR) by single-atom catalysts (SACs) provides a promising route for converting CO into value-added C₁ products, but the rational discovery of efficient SACs remains challenging because of the large configurational space and complex reaction network. Herein, we chose two-dimensional periodic macrocyclic frameworks with MN₄ coordination centers as the SACs and established a multilevel screening strategy by combining first-principles calculations and machine learning (ML). After evaluating thermodynamic stability, dissolution resistance, *CO adsorption, and hydrogen evolution reaction suppression, 90 candidates were screened out from 156 SACs for further analysis. The hydrogenation of *CO to *CHO was identified as a key step for deep CO reduction, and ΔG_(*CHO‑*CO) was selected as the main descriptor. An ExtraTrees regression model trained on the screened SACs achieved high predictive accuracy, with R₂ values of 0.947 and 0.925 for the training and test sets, respectively. By scanning the full reaction pathway of CORR (CH₄ as the final product) on the SACs with relatively low ΔG_(*CHO‑*CO) value, the FeC₈N₈ was found to exhibit the highest catalytic activity among the examined candidates, with a low limiting potential (U_L) of −0.30 V. To accelerate catalyst evaluation, ΔG_(*CHO‑*CO), which describes the first hydrogenation of adsorbed *CO, was selected as the key descriptor and predicted by ML algorithm. Full-pathway calculations were subsequently carried out for catalysts with relatively low ΔG_(*CHO‑*CO) values to validate the descriptor-guided screening results. 2026-09-28T00:00:00Z Text Journal contribution 10.1021/acs.jpcc.6c05023.s001 https://figshare.com/articles/journal_contribution/High-Throughput_Screening_of_MN_sub_4_sub_Coordinated_Macrocyclic_Frameworks_for_CO_Reduction_by_Combining_First-Principles_Calculations_and_Machine_Learning/34018269 CC BY-NC 4.0