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        "author": "Chi Chen",
        "author_email": "chc273@eng.ucsd.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "# Minimum Energy Path Tools\n[![CircleCI](https://circleci.com/gh/chc273/mep.svg?style=svg)](https://circleci.com/gh/chc273/mep)\n[![Coverage Status](https://coveralls.io/repos/github/chc273/mep/badge.svg?branch=master)](https://coveralls.io/github/chc273/mep?branch=master)\n\n## Introduction \nThis package contains various methods for finding the minimal energy path in atom simulations.\n\nCurrently the following methods are implemented:\n\n> Nudged elastic band method [1]\n\n> Climbing image nudged elastic band method [2]\n\n## How to use\n\n### Regular NEB\n```python\n\nfrom mep.optimize import ScipyOptimizer\nfrom mep.path import Path\nfrom mep.neb import NEB\nfrom mep.models import LEPS\n\nleps = LEPS() # Test model \nop = ScipyOptimizer(leps) # local optimizer for finding local minima\nx0 = op.minimize([1, 4], bounds=[[0, 4], [-2, 4]]).x # minima one\nx1 = op.minimize([3, 1], bounds=[[0, 4], [-2, 4]]).x # minima two\n\n\npath = Path.from_linear_end_points(x0, x1, 101, 1)  # set 101 images, and k=1\nneb =NEB(leps, path) # initialize NEB\nhistory = neb.run(verbose=True) # run\n\n```\n\nThe results will be like the following\n\n![LEPS example](./assets/leps.gif) ![LEPS_NEB](./assets/leps_ea.png) \n\n\nSimilar results can be obtained using the LEPS model with harmonics `LEPSHarm`\n\n![LEPSHarm_example](./assets/lepsharm.gif) ![LEPS_NEB](./assets/lepsharm_ea.png) \n\n### CI-NEB\nEvery thing is the same except that \n```python\nneb =NEB(leps, path, climbing=True, n_climbs=1) # set one image for climbing\nhistory = neb.run(verbose=True, n_steps=10, n_climb_steps=100) # run normal NEB for 10 steps and then switch to CINEB\n```\n\nFor comparison, normal NEB using `LEPSHarm` potential with 5 images gives the following\n\n![LEPS example](./assets/lepsharm_nocineb.png) ![LEPS_NEB](./assets/lepsharm_ea_nocineb.png) \n\nWith CI-NEB \n\n![LEPS example](./assets/lepsharm_cineb.png) ![LEPS_NEB](./assets/lepsharm_ea_cineb.png) \n\nWe can see that using only 5 images, the CINEB gets `Ea = 3.63 eV`, the same as the one we ran with 101 images!\nWith only normal NEB, however, this `Ea` value is substantially smaller (`3.25 eV`). \n## References\n\n> [1] Henkelman, G., & J\u00f3nsson, H. (2000). Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points. The Journal of chemical physics, 113(22), 9978-9985.\n\n> [2] Henkelman, G., Uberuaga, B. P., & J\u00f3nsson, H. (2000). A climbing image nudged elastic band method for finding saddle points and minimum energy paths. The Journal of chemical physics, 113(22), 9901-9904.\n\n",
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        "summary": "Minimal energy path tools for atomistic systems",
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