PASTA: Python Algorithms for Searching Transition stAtes

S. Kundu

doi:10.1016/j.cpc.2018.06.026

2018-12-01

S. Kundu

Satadeep Bhattacharjee

Seung‐Cheol Lee *

Manish Jain *

DOI：10.1016/j.cpc.2018.06.026

原文链接

查看原文

摘要

En 中文

Chemical reactions are often associated with an energy barrier along the reaction pathway which hinders the spontaneity of the reaction. Changing the energy barrier along the reaction pathway allows one to modulate the performance of a reaction. We present a module, Python Algorithms for Searching Transition stAtes (PASTA), to calculate the energy barrier and locate the transition state of a reaction efficiently. The module is written in python and can perform nudged elastic band, climbing image nudged elastic band and automated nudged elastic band calculations. These methods require the knowledge of the potential energy surface (and its gradient along some direction). This module is written such that it works in conjunction with density functional theory (OFT) codes to obtain this information. Presently it is interfaced with three well known OFT packages: Vienna Ab initio Simulation Package (VASP), Quantum Espresso and Spanish Initiative for Electronic Simulations with Thousands of Atoms (SIESTA). This module is easily extendable and can be interfaced with other OFT, force-field or empirical potential based codes. The uniqueness of the module lies in its user-friendliness. For users with limited computing resources, this module will be an effective tool as it allows to perform the calculations image by image. On the other hand, users with plentiful computing resources (such as users in a high performance computing environment) can perform the calculations for large number of images simultaneously. This module gives users complete flexibility, thereby enabling them to perform calculations on large systems making the best use of the available resources. Program summary Program Title: PASTA Program files doi : http://dx.doi.org/10.17632/rv7fdm5gkf.1 Licensing provisions: BSD 3-clause Programming language: Python External routines/libraries: numpy, matplotlib Nature of problem: Most of the reactions have an energy barrier on their reaction pathway. This energy barrier affects the progress of the reaction. Solution method: We implement the NEB, CI-NEB and AutoNEB method to locate transition state and estimate the energy barrier. Our module works with density functional theory codes: VASP, SIESTA and Quantum Espresso presently. (C) 2018 Elsevier B.V. All rights reserved.

Keyword:

NEB

Cl-NEB

AutoNEB

Transition state

Energy barrier

Python

DFT