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Cu-based metal-organic frameworks have attracted much attention for electrocatalytic CO 2 reduction, but they are generally instable and difficult to control the product selectivity. We report flexible Cu(I) triazolate frameworks as efficient, stable, and tunable electrocatalysts for CO 2 reduction to C 2 H 4 /CH 4 . By changing the size of ligand side groups, the C 2 H 4 /CH 4 selectivity ratio can be gradually tuned and inversed from 11.8 : 1 to 1 : 2.6, giving C 2 H 4 , CH 4 , and hydrocarbon selectivities up to 51 %, 56 %, and 77 %, respectively. After long-term electrocatalysis, they can retain the structures/morphologies without formation of Cu-based inorganic species. Computational simulations showed that the coordination geometry of Cu(I) changed from triangular to tetrahedral to bind the reaction intermediates, and two adjacent Cu(I) cooperated for CÀ C coupling to form C 2 H 4 . Importantly, the ligand side groups controlled the catalyst flexibility by the steric hindrance mechanism, and the C 2 H 4 pathway is more sensitive than the CH 4 one. Electrocatalytic carbon dioxide reduction reaction(CO 2 RR) is promising to reduce the use of fossil fuels and achieve global carbon neutrality. [1] Among various catalytic active centers, only copper has demonstrated high selectivity to the valuable hydrocarbons. [2] Copper-based catalysts can also show selectivity to aldehydes, ketones, carboxylic acids and alcohol. [3] Cu-based inorganic catalysts have been extensively studied, but elucidation of the structure-performance relationship remains a great challenge because of the lack of well-defined structures of the active sites.As molecule-based crystalline materials with diversified and well-defined pore-surface structures, metal-organic frameworks (MOFs) have been widely studied in various fields including catalysis. [4] Many MOFs, including the classic ones consisting of Cu(II), have been studied for CO 2 RR. [5] However, Cu(II)-based MOFs usually serve as the precursors of inorganic catalysts such as Cu and Cu 2 O. [6] In the few Cu(II)-based MOF catalysts stable in CO 2 RR, the Cu(II) ions are stabilized by chelating ligands. [7] Considering that Cu(II) needs to be reduced to Cu(I) during the CO 2 RR processes, and the common coordination geometries of Cu(I) and Cu(II) are quite different, Cu(I)-based MOFs should be more suitable to serve as stable CO 2 RR catalysts. [8] Metal azolate frameworks (MAFs) are a unique kind of MOFs with outstanding thermal and chemical stabilities. [9] Compared with other types of ligands, azolates are particularly useful for linking Cu(I) ions to form stable MOFs, but have been scarcely used for CO 2 RR. [10] [Cu(detz)] (MAF-2 or MAF-2E, Hdetz = 3,5-diethyl-1,2,4-triazole) is a classic Cu(I)-based MOF, in which trigonal Cu(I) ions are bridged to form dimers (Cu•••Cu 3.4 Å) with both faces exposed on the pore surface (Figure 1). [11] It is well known that the bicopper active sites are essential to CÀ C coupling in CO 2 RR for the valuable C 2 products. [12] The coordination mic...

Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based potentials derived from known three-dimensional structures of proteins and/or protein–ligand complex structures. This study seeks to improve a knowledge-based protein–ligand potential based on a distance-scale finite ideal-gas reference (DFIRE) state (DLIGAND) by expanding the representation of protein atoms from 13 mol2 atom types to 167 residue-specific atom types, and employing a recently updated dataset containing 12,450 monomer protein chains for training. We found that the updated version DLIGAND2 has a consistent improvement over DLIGAND in predicting binding affinities for either native complex structures or docking-generated poses. More importantly, DLIGAND2 has a 52% increase over DLIGAND in enrichment factors in top 1% predictions based on the DUD-E decoy set, and consistently improves over Autodock Vina and other statistical energy functions in all three benchmark tests. We further found that DLIGAND2 outperforms empirical and machine-learning methods compared for virtual screening on new targets that are not homologous to the DUD-E training set. Given the best performance as a parameter-free statistical potential and among the best in all performance measures, DLIGAND2 should be useful for re-assessing the poses generated by docking software, or acting as one term in other scoring functions. The program is available at https://github.com/sysu-yanglab/DLIGAND2 . Electronic supplementary material The online version of this article (10.1186/s13321-019-0373-4) contains supplementary material, which is available to authorized users.

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