Materials Informatics project at the Lawrence Livermore National Laboratory aims to accelerate materials discovery, optimization and scale-up processes by combining<i> automated information extraction, machine learning, data analytics and experimental validations to pinpoint critical reaction parameters in a given synthesis</i> to aid in the development of advanced materials that are highly desirable to the lab as well as to the society. In this regard, we are developing information ingest pipeline to take unstructured data (scientific literature) and generate structured knowledge database, that are machine readable, which will allow us to perform data analytics to discover and improve chemical synthesis pathways and optimization processes. In this presentation, discussion on applying machine learning algorithms for information extraction from literature, as well as application of computer vision techniques to extract relevant information will be discussed. Development of such a tool in chemical sciences will significantly shorten the time for a researcher to canvas his/her field of research as well as identify key reaction steps and insights to materials synthesis and optimization by connecting multiple variables from multiple sources simultaneously.

Application of machine learning and computer vision techniques in information extraction and property predictions

255th National Meeting (2018)

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