{ "info": { "author": "Qi Wang", "author_email": "qwang.mse@gmail.com", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Developers", "Intended Audience :: Science/Research", "Programming Language :: Fortran", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], "description": "# amlearn\nMachine Learning Package for Amorphous Materials.\n\nTo featurize the heterogeneous atom site environments in amorphous materials,\nwe can use `amlearn` to derive 1k+ candidate features that encompass short- (SRO)\nand medium-range order (MRO) to describe the packing heterogeneity around each atom site. \n(See the following example figure for combining site features and machine learning (ML) to predict the \ndeformation heterogeneity in metallic glasses). \n\nCandidate features include recognized signatures\nsuch as coordination number (CN), Voronoi indices, characteristic motifs,\nvolume metrics (atomic/cluster packing efficiency), i-fold symmetry indices,\nbond-orientational orders, symmetry functions (originally proposed to fit\nML interatomic potentials and recently gained success in featurizing disordered\nmaterials), as well as our recently proposed highly interpretable and generalizable\ndistance/area/volume interstice distribution features (to be published).\n\nIn `amlearn`, We integrate Fortran90\nwith Python (using f2py) to achieve combination of the flexibility and\nfast-computation (>10x times faster than pure Python) of features.\nPlease refer to the SRO and MRO feature representations in `amlearn.featurize`.\n\n\n
\"amlearn\"
\n \n\nIn addition, wrapper classes and utility functions for machine\nlearning algorithms supported by scikit-learn are also included. \n\n\n## Installation\n\nBefore installing amlearn, please install numpy (version 1.7.0 or greater) first.\n\nWe recommend to use the conda install.\n\n```sh\nconda install numpy\n```\n\nor you can find numpy installation guide from [Numpy installation instructions](https://www.scipy.org/install.html).\n\n\nThen, you can install amlearn. 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