
Variety of input/output formats like CIF, Gaussian, XYZ, VASP. Highly flexible classes for representation of Element, Site, Molecule, Structure objects, Nearest Neighbors. Note- Only getting electronic configuration, atomic number, or any other very basic material properties does not account for material analysis. It is a robust, open-source, and widely used Python library for material analysis. Pymatgen is a short form for Python Materials Genomics. Many representations of properties elements can be converted in a mathematical format of vectors and matrices (like using one-hot vector encoding for describing an electronic configuration of an element ) to pass them as an input to a machine learning algorithm. In the dictionary, it is described as a word or expression used to describe or identify something.Ī descriptor is used to describe the compound to computing algorithms. Analyzing the same data can predict the magnetic properties of unknown magnetic compounds( whose properties are still novel) using a machine learning approach. Use-case: This study is also getting integrated with computer science to get better and precise insights into data without having much practical implementation.Įxample: If one has a database of, say, Mn(Manganese) compounds and their magnetic behavior. Scientifically-It’s a study to get insight into a material’s fundamental properties to determine whether the material is suitable for its intended use-case or needs some doping (or any other approach) to make it well qualified for the purpose. In layman’s terms, it is a field of study to analyze materials and their properties. With this kind of approach, one can also redefine problems outside usual boundaries and reach solutions using a new understanding of impenetrable situations which early considered impossible to get.
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