Machine learning for materials chemistry
Machine learning has huge potential as a tool to investigate new materials and new applications of existing materials, as well as to streamline and focus future experimentation through rapid screening. This Collection Explores the use of machine learning in all aspects of materials chemistry, from discovering and designing new materials to modelling and optimising their performance, defining structure-property relationships and identifying new applications.
Editors
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Ting Liao
Queensland University of Technology, Australia
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Taylor Sparks
University of Utah, USA
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Hao Yu
Southern University of Science and Technology, China
This Collection is no longer open for submissions.
In addition to papers on machine learning for materials chemistry, Scientific Reports welcomes all original research in the field of materials science. To browse our latest articles in materials science, click here.
