The　high　porosity　and　tunable　chemical　functionality　of　metal-organic　frameworks　(MOFs)　make　it　a　promising　catalyst　design　platform.　High-throughput　screening　of　catalytic　performance　is　feasible　since　the　large　MOF　structure　database　is　available.　In　this　study,　we　report　a　machine　learning　model　for　high-throughput　screening　of　MOF　catalysts　for　the　CO2　cycloaddition　reaction.　The　descriptors　for　model　training　were　judiciously　chosen　according　to　the　reaction　mechanism,　which　leads　to　high　accuracy　up　to　97%　for　the　75%　quantile　of　the　training　set　as　the　classification　criterion.　The　feature　contribution　was　further　evaluated　with　SHAP　and　PDP　analysis　to　provide　a　certain　physical　understanding.　12,415　hypothetical　MOF　structures　and　100　reported　MOFs　were　evaluated　under　100　degrees　C　and　1　bar　within　one　day　using　the　model,　and　239　potentially　efficient　catalysts　were　discovered.　Among　them,　MOF-76(Y)　achieved　the　top　performance　experimentally　among　reported　MOFs,　in　good　agreement　with　the　prediction.　(c)　2024　Institute　of　Process　Engineering,　Chinese　Academy　of　Sciences.　Publishing　services　by　Elsevier　B.V.　on　behalf　of　KeAi　Communications　Co.,　Ltd.　This　is　an　open　access　article　under　the　CC　BY-NC-ND　license　(http://creativecommons.org/licenses/by-nc-nd/4.0/).