{"id":"W2766362701","doi":"10.1021/acs.chemmater.7b03500","title":"Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning","year":2017,"lang":"en","type":"article","venue":"Chemistry of Materials","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":475,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Basic Energy Sciences; Division of Materials Research; Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Massachusetts Institute of Technology; U.S. Department of Energy; National Science Foundation","keywords":"Bottleneck; Computer science; Transformative learning; Compiler; Heuristic; Throughput; Nanotechnology; Artificial intelligence; Machine learning; Materials science; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231112383633575,"score_gpt":0.2511678201876742,"score_spread":0.2388566963513384,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}