Graph networks are a new machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization. Here, we develop universal MatErials Graph Network (MEGNet) models for accurate property prediction in both molecules and crystals. We demonstrate that the MEGNet models outperform prior ML models such as the SchNet in …
read morePymatgen (Python Materials Genomics) is a robust, open-source Python library for materials analysis. These are some of the main features: Highly flexible classes for the representation of Element, Site, Molecule, Structure objects. Extensive input/output support, including support for VASP, ABINIT, CIF, Gaussian, XYZ, and many other file formats. Powerful analysis …
read moreGraph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous …
read moreDeepchem is a machine learning library with multiple scientific tools to assist in research. These tools range from analyzing protein structures to counting cells in a microscopy image. As an open source project anyone is free to download, use, and improve deepchem.
read moreComparison of the mean absolute error (MAE) on a test set in the formation energy (Ef in eV/atom), the band gap (Eg in eV, the superscript nz refers to datasets restricted to non-zero band gaps), the refractive index (n) for MODNet, two variants of MEGNet and SISSO as a function …
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