A　paradigm　for　material　synthesis　is　demonstrated　under　the　guidance　of　machine　learning　as　well　as　the　designed　wet-lab　experiments.　This　work　begins　with　a　series　of　well-tailored　experiments,　including　the　synthesis　of　C/N-codoped　TiO2　nanoparticles　with　exposed　anatase　{001}　facets　and　its　photocatalytic　performance　evaluations.　In　advance　of　applying　the　machine　learning,　the　underlying　synergistic　effects　are　revealed　and　expounded　in　detail　purely　based　on　wet-lab　experiments.　Then,　the　idea　of　active　learning　is　adopted　by　implementing　a　multi-step　workflow　to　iterate　the　experiments　around　preferred　synthesis　conditions,　and　unravel　the　optimum　synthesis　parameters　with　the　assistance　of　machine　learning.　In　this　work,　the　prediction　models　are　built　on　a　solid　physical　and　chemical　understanding,　rather　than　a　＇black　box　approach＇.　To　this　end,　the　designed　experiments　as　well　as　the　bootstrapping　technique　together　guarantee　a　good　prediction　with　less　training.　The　compatibility　of　the　artificial　neural　network　and　wet-lab　experiment　also　endows　the　current　strategy　with　several　innate　advantages.　Moreover,　controlled　experiments　with　different　starting　materials　were　carried　out.　The　results　suggest　that,　in　an　effort　to　ensure　more　accurate　predictions,　further　restrictions　could　be　placed　on　data　collection　prior　to　the　stage　of　introducing　machine　learning,　and　the　design　of　wet-lab　experiment　plays　a　crucial　role　in　facilitating　machine　learning.　Lastly,　the　future　prospect　for　machine　learning-assisted　material　synthesis　is　briefly　discussed　based　on　the　current　work.　(C)　2021　Elsevier　B.V.　All　rights　reserved.