The　process　of　discovering　and　developing　new　materials　currently　requires　considerable　effort,　time,　and　expense.　Machine　learning　(ML)　algorithms　can　potentially　provide　quick　and　accurate　methods　for　screening　new　materials.　In　the　present　work,　the　features　of　the　metal　organic　frameworks　(MOFs)　as　a　catalyst　for　fixing　carbon　dioxide　into　cyclic　carbonate　were　extracted　to　build　a　data　set,　which　were　collected　from　the　experimental　results　of　approximately　100　published　papers.　Classifiers　were　trained　with　the　data　set　with　various　ML　algorithms,　including　support　vector　machine　(SVM),　K-nearest　neighbor　classification　(KNN),　decision　trees　(DT),　stochastic　gradient　descent　(SGD),　and　neural　networks　(NN),　to　predict　the　catalytic　performance.　The　ML　models　were　trained　on　80%　of　the　data　set　and　then　tested　on　the　remaining　20%　to　predict　the　carbon　dioxide　fixation　ability.　The　trained　ML　model　was　extended　to　explore　1311　hypothetical　MOFs,　and　some　structures　displayed　a　strong　catalytic　ability.　Finally,　the　six　best　metal　ions　(Mn,　V,　Cu,　Ni,　Zr　and　Y)　and　four　best　ligands　(tactmb,　tdcbpp,　TCPP,　H3L)　were　determined.　These　six　metals　and　four　ligands　could　be　combined　into　24　MOFs,　which　are　strongly　potential　catalysts　for　carbon　dioxide　fixation.　Using　machine　learning　methods　can　speed　up　the　screening　of　materials,　and　this　methodology　is　promising　for　application　not　only　to　MOFs　as　catalysts　but　also　in　many　other　materials　science　projects.　(C)　2021　The　Chinese　Ceramic　Society.　Production　and　hosting　by　Elsevier　B.V.