Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning

By Edward Kim, Kevin Huang, Adam Saunders, Andrew McCallum, Gerbrand Ceder, E. Olivetti. 2017

430 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/e69c18f7e5c424e57adb0d93445997d348ae2cc9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Materials Chemistry

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

The Role of Machine Learning in the Understanding and Design of Materials (2020, background): “For example, to address the lack of data for material synthesis, Kim et al. 48,49 performed Figure 2.”
Machine learning for molecular and materials science (2018, background): “Text mining is applied to answer many different research questions, ranging from the discovery of novel drug–protein target associations, or analysis of high throughput experiments, to developing systematic materials databases.(84) Due to the heterogeneous nature of written resources, the automated ”
Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing (2018, methodology): “Such methods may also increase learning from diagnosis, by consolidating research output in singular databases, drawing automated inferences from the data, and in the future perhaps aggregating the experience and knowledge base via natural language processing of existing research papers and material”
Machine learning: Accelerating materials development for energy storage and conversion (2020, methodology): “The text mining technology has been widely used in chemical and materials science.(206,207) Besides, machines thirst for the ability of one shot learning, learning a class from a handful or even a single labeled example, which originates in human beings and can solve the problem of limited datasets.”
Recent advances and applications of deep learning methods in materials science (2021, methodology): “Such data has been used to predict synthesis maps for titania nanotubes [310], various binary and ternary oxides [318], and perovskites [319].”
Machine learning for heterogeneous catalyst design and discovery (2018, background): “Some advances on the text-mining front have already been made in the chemistry(116) and materials science communities.(117,118) Tools are needed to”