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Extraction of optimal synthesis conditions from scientific literature using a knowledge graph
Autonomous experiments for material synthesis have been developing with increasing speed. With the expanding search space for accessible materials, the efficient collection of prior knowledge, particularly synthesis conditions, before autonomous experiments is crucial in reducing the number of trials. In this study, we developed a workflow that systematically extracts synthesis conditions from scientific literature. We constructed a knowledge graph to test the simple hypothesis that significant correlations exist between the physical properties and synthesis conditions that appear nearby in a text. The performance of this scheme was demonstrated by extracting the appropriate thin-film synthesis conditions for conductive Nb-doped TiO2. The proposed methodology is expected to accelerate autonomous material synthesis experiments.
Extraction of optimal synthesis conditions from scientific literature using a knowledge graph
Autonomous experiments for material synthesis have been developing with increasing speed. With the expanding search space for accessible materials, the efficient collection of prior knowledge, particularly synthesis conditions, before autonomous experiments is crucial in reducing the number of trials. In this study, we developed a workflow that systematically extracts synthesis conditions from scientific literature. We constructed a knowledge graph to test the simple hypothesis that significant correlations exist between the physical properties and synthesis conditions that appear nearby in a text. The performance of this scheme was demonstrated by extracting the appropriate thin-film synthesis conditions for conductive Nb-doped TiO2. The proposed methodology is expected to accelerate autonomous material synthesis experiments.
Extraction of optimal synthesis conditions from scientific literature using a knowledge graph
Shigeru Kobayashi (Autor:in) / Norikazu Kuwashiro (Autor:in) / Fumiaki Itoh (Autor:in) / Dai Sakurai (Autor:in) / Taro Hitosugi (Autor:in)
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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