Semi-supervised machine-learning classification of materials synthesis procedures  

作　　者：Haoyan Huo Ziqin Rong Olga Kononova Wenhao Sun Tiago Botari Tanjin He Vahe Tshitoyan Gerbrand Ceder 

机构地区：[1]Department of Materials Science and Engineering,University of California,Berkeley,CA 94720,USA [2]Materials Sciences Division,Lawrence Berkeley National Laboratory,Berkeley,CA 94720,USA [3]Google LLC,1600 Amphitheatre Parkway,Mountain View,CA 94043,USA

出　　处：《npj Computational Materials》2019年第1期562-568,共7页计算材料学（英文）

基　　金：Funding to support this work was provided by the Energy&Biosciences Institute through the EBI-Shell program,Office of Naval Research(ONR)Award #N00014-14-1-0444;the National Science Foundation under Grant No 5710003959.

摘　　要：Digitizing large collections of scientific literature can enable new informatics approaches for scientific analysis and meta-analysis.However,most content in the scientific literature is locked-up in written natural language,which is difficult to parse into databases using explicitly hard-coded classification rules.In this work,we demonstrate a semi-supervised machine-learning method to classify inorganic materials synthesis procedures from written natural language.Without any human input,latent Dirichlet allocation can cluster keywords into topics corresponding to specific experimental materials synthesis steps,such as“grinding”and“heating”,“dissolving”and“centrifuging”,etc.Guided by a modest amount of annotation,a random forest classifier can then associate these steps with different categories of materials synthesis,such as solid-state or hydrothermal synthesis.Finally,we show that a Markov chain representation of the order of experimental steps accurately reconstructs a flowchart of possible synthesis procedures.Our machine-learning approach enables a scalable approach to unlock the large amount of inorganic materials synthesis information from the literature and to process it into a standardized,machine-readable database.

关 键 词：synthesis. SYNTHESIS STEPS 

分 类 号：O62[理学—有机化学]