Estimating　Density　Functional　Theory　(DFT)　calculation　error　is　an　important　while　challenging　task　in　computational　material　science.　The　calculation　contains　inherent　errors　due　to　improper　input　parameters　and　approximated　exchange-correlation　functional.　In　this　paper,　we　present　a　data-driven　approach　of　using　machine　learning　techniques　to　estimate　the　error　of　DFT　calculation.　We　prepare　the　data　by　high-throughput　first　principle　DFT　simulation　and　experimental　data　collection.　The　single-hidden　layer　back　propagation　feedforward　neural　network　(SLBPFN)　constructed　based　on　the　proposed　cross　validation　algorithm,　and　support　vector　machine　for　regression　(SVR)　techniques　are　employed　to　build　regression　models　to　predict　the　DFT　calculation　error.　As　a　demonstration,　the　developed　regression　models　are　used　to　predict　errors　in　calculating　elastic　constants　of　cubic　binary　alloys.　It　has　been　demonstrated　that　the　machine　learning　techniques　can　predict　DFT　calculation　error　of　elastic　constants　with　an　acceptable　accuracy.　It　also　shows　the　BP　neural　network　built　by　our　proposed　cross　validation　algorithm　can　provide　a　better　prediction.　Our　study　is　a　first-invasive　work　of　using　machine　learning　techniques　to　estimate　the　errors　in　calculating　elastic　constants　of　binary　alloys.　(C)　2017　Elsevier　B.V.　All　rights　reserved.