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Found 3 papers, 1 papers with code
High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses
Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited.
Materials Science
The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques.
Materials Science Computational Physics
A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection
An analysis of high-dimensional data can offer a detailed description of a system but is often challenged by the curse of dimensionality.
