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Microbubble flows in superwettable fluidic channels
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Materials metadata: as a custom schema, as directories, or in a data package
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Machine Learning-Based Experimental Design in Materials Science
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Leveraging Segmentation of Physical Units through a Newly Open Source Corpus
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SuperMat: Construction of a linked annotated dataset from superconductors-related publications
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Structure prediction of boron-doped graphene by machine learning
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MDTS: automatic complex materials design using Monte Carlo tree search
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Prediction and optimization of epoxy adhesive strength from a small dataset through active learning
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Automatic Experimental Data Collection System Using a Wireless LAN Capable SD Card as an IoT Device
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Free Analysis and Visualization Programs for Electrochemical Impedance Spectroscopy Coded in Python
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Monte Carlo tree search for materials design and discovery
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