Breakthrough　research　may　signal　shifts　in　science,　technology,　and　innovation　systems.　Early　identification　of　breakthrough　research　is　important　not　only　for　scientists,　but　also　for　policy　makers　and　R&D　experts　in　developing　R&D　strategies　and　allocating　R&D　resources.　Researchers　mostly　use　scientific　papers　data　to　identify　potential　breakthrough　research,　but　they　rarely　make　use　of　Twitter　data　related　to　scientific　research　and　machine　learning　methods.　Analysis　of　Twitter　data　is　of　great　significance　for　us　to　understand　the　public＇s　perception　of　potential　breakthrough　research　and　to　identify　potential　breakthrough　research.　Machine　learning　methods　can　assist　us　in　predicting　the　trend　of　events　by　utilizing　prior　knowledge　and　experience.　Therefore,　this　paper　proposes　a　framework　for　identifying　potential　breakthrough　research　using　machine　learning　methods　with　scientific　papers　and　Twitter　data.　We　select　solar　cells　as　a　case　study　to　verify　the　valid　and　flexible　of　this　framework.　In　this　case,　we　use　machine　learning　method　to　discover　potential　breakthrough　research　from　scientific　papers,　and　we　use　Twitter　data　mining　to　analyze　Twitter　users＇　sense　of　and　response　to　the　discovered　potential　breakthrough　research,　which　aims　to　achieve　a　more　extensive　and　diverse　assessment　of　the　discovered　potential　breakthrough　research.　This　paper　contributes　to　identifying　potential　breakthrough　research,　as　well　as　understanding　the　emergence　and　development　of　breakthrough　research.　It　will　be　of　interest　to　R&D　experts　in　the　field　of　solar　cell　technology.