{
    "data_id": "43356",
    "name": "Comprehensive-database-of-Minerals",
    "exact_name": "Comprehensive-database-of-Minerals",
    "version": 1,
    "version_label": "v1.0",
    "description": "This dataset is the collection of 3112 minerals, their chemical compositions, crystal structure, physical and optical properties. The properties that are included in this database are the Crystal structure, Mohs Hardness, Refractive Index, Optical axes, Optical Dispersion, Molar Volume, Molar, Mass, Specific Gravity, and Calculated Density.\nIntroduction\nThe term dielectric is applied to a class of materials - usually solids - that are poor conductors of\nelectricity. Dielectrics are of significant technological and industrial importance, being essential\nfunctional components of almost all electronic devices. For most of these applications, they are\nrequired to be mechanically tough and thermally robust. The defining physical attribute of a\ndielectric is electric polarizability which is the tendency for charges to be non-uniformly\ndistributed across a chemical bond. Most dielectrics contain dipoles due to their ionic bonds or\ncovalent bonds with strong ionic nature. At a macroscopic scale, this implies that an external\nelectric field can interact with these charges and result in various optical and electric phenomena.\nOptically, dielectrics can be transparent, opaque, or vitreous. They can also be isotropic, \nbiaxial, or fully anisotropic. The luster of gem minerals such as emerald, sapphire, and ruby is due to\ntheir high refractive index which causes white light to be split into its components. The presence\nof two refractive indices in a material can result in an incident beam being split into two rays\nthat interfere with each other. This common phenomenon is called Birefringence. These effects\nare made use of in many commercially important applications such as transparent conductive\noxides, liquid crystal displays, medical diagnostics, stress sensing, light modulation, etc.\nAs an example, transparent conducting oxides (TCO) are derived from dielectrics by doping oxides\nwith impurity atoms. TCOs do not absorb light in the visible spectrum rendering them transparent\nand are also conductors of charge. The most important application of TCOs is as the top\nelectrode of solar cells where they allow light to fall on a semiconducting layer while capturing\nthe released hole\/electron to generate current. Airplane windshields have a thin coating of a\nTCO material on them that is used to generate heat by passing a current. This is necessary to\nkeep the glass defrosted allowing the pilot visibility to navigate. Other applications of TCOs is\nas substrates in electronics, flexible displays, high definition TVs, and the screens of mobile smart\ndevices.\nThe figure for merit for optical phenomena is the refractive index, which is defined as the ratio\nof the speed of light in the medium to the speed of light in vavacuum.\nProvenance of Data\nThe list of minerals with individual pages in Wikipedia is given at:\nhttps:\/\/en.wikipedia.org\/wiki\/List_of_minerals. The get method of the requests library is used\nto retrieve this page and the content is parsed using BeautifulSoup  a python library specifically\nengineered for parsing html and lxml content. The URLs for all the minerals given in this page is\nextracted using their href attribute and are stored in a dictionary, along with the mineral name.\nEach of the webpages has textual information on the mineral (origin, etymology, variety, history\netc.), images (cleavages, and other data) as well as an Infobox on the right that tabulates some\ncommon mineral properties such as category, formula, strunz classification, crystal structure,\nunit cell, Mohs hardness, color, cleavage, fracture, luster, diaphaneity, specific gravity, optical\nproperties a and refractive index. The soup object for the page is retrieved and the table element\nwith class name infobox is extracted. The specified row heading and row data are then read into\na dictionary which is wrapped in a class object. A class method writes this data into a csv file while\nanother method writes the text from the webpage into a text file. \nThe American Mineralogist Crystal Structure Database at\nhttp:\/\/rruff.geo.arizona.edu\/AMS\/amcsd.php has a list of over 4000 minerals with their cif files.\nThe name and the URL of all these minerals are found at http:\/\/rruff.geo.arizona.edu\/AMS. From\nhere, each mineral name and the corresponding URL is extracted using the approach outlined\nabove. Accessing each page, we find the crystallographic information of the mineral. The a,b,c edge\nlengths and alpha, beta, gamma - unit cell angles are given at the top followed by a list of\nall atoms and their x,y,z positions. The header is extracted and stored in a pandas dataframe\nwhile the atomic species and their positions are saved into a separate CSV file. This is repeated\nfor all the 4000 minerals. Before inclusion into the machine learning stage of this study, each of these \ncif files are read and parsed into a vector with each cell corresponding to an element of the periodic table and the number\nof atoms of the element in the formula is counted as the cell value. This is detailed further in\nthe data processing part of the project.\nCompared to other properties, dispersion of minerals has been hard to find. Dispersion values of\n60 minerals found at: http:\/\/gemologyproject.com\/wiki.\nThe chemical formula, molar mass, molar volume, and calculated density are available for all minerals. The availability of other properties vary.\nChemical Formula\nThe chemical formula has been parsed so that the number of each element has been separated tabulated. For example, the mineral Quartz has the formula 'SiO2' - so that the corresponding entry for the column 'Silicon' is 1 and the entry for 'Oxygen' is 2. The entries for all the other elements are 0.\nIn this way, the chemical formula for each mineral is converted into a vector where each column corresponds to an element in the periodic table and the value corresponds to the number of atoms of the element in a formula unit of the mineral.\nIn addition to the pure elements, ionic species such as carbonate, phosphate, nitrate, cyanide, hydrated water, etc are also counted separately.\nMolar Mass\nThe molar mass of the mineral is calculated by adding together the mass of each atom in a mole of the mineral.\nMolar mass = Summation( no of atoms * mass of each atom)\nMolar Volume\nThe molar volume of the mineral is calculated by adding together the volume of each atom in a mole of the mineral.\nMolar volume = Summation( no of atoms * volume of each atom)\nRefractive Index\nThe refractive index of the mineral is defined as the ratio of the speed of light in the mineral to the speed of light in free space.\nThis is a function of the frequency of light. The RI of blue light is not the same as the RI of red light in the same mineral. This variation is measured by 'dispersion'.\nMohs Hardness\nMohs hardness is a qualitative measure for the hardness of a mineral that is frequently used by\nthe geologist. Diamond (hardest mineral) is given the highest value of 10 and talc (softest\nmineral) is given the value of 1. A mineral that can scratch a second mineral has a higher Mohs\nhardness. In this way, all the minerals can be ranked on a relative scale of hardness. It is not exactly clear what physical parameter is represented by the Mohs Hardness. Several absolute scales for hardness such as toughness, yield strength, etc. are known from the mechanics of materials, however, none of them seem to correspond exactly to Mohs Hardness. However, this remains a very intuitive way to understand the physical property of a material.",
    "format": "arff",
    "uploader": "Elif Ceren Gok",
    "uploader_id": 30125,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-23 12:38:06",
    "update_comment": null,
    "last_update": "2022-03-23 12:38:06",
    "licence": "CC0: Public Domain",
    "status": "active",
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    "url": "https:\/\/www.openml.org\/data\/download\/22102181\/dataset",
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            "uploader": "38960",
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