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Found 4 papers, 2 papers with code
Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization
We utilize a recently developed topic modeling method called SeNMFk, extending the standard Non-negative Matrix Factorization (NMF) methods by incorporating the semantic structure of the text, and adding a robust system for determining the number of topics.
CRYSPNet: Crystal Structure Predictions via Neural Network
Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics.
Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.
Machine learning modeling of superconducting critical temperature
Separate regression models are developed to predict the values of $T_{\mathrm{c}}$ for cuprate, iron-based, and "low-$T_{\mathrm{c}}$" compounds.
