The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 16, 2021

Filed:

May. 10, 2018
Applicant:

Shenzhen Jingtai Technology Co., Ltd., Guangdong, CN;

Inventors:

Guangxu Sun, Guangdong, CN;

Chunwang Peng, Guangdong, CN;

Mingjun Yang, Guangdong, CN;

Hui Xiao, Guangdong, CN;

Yang Liu, Guangdong, CN;

Jian Ma, Guangdong, CN;

Lipeng Lai, Guangdong, CN;

Shuhao Wen, Guangdong, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01N 33/48 (2006.01); G01N 33/50 (2006.01); G06F 30/20 (2020.01); G06F 9/48 (2006.01); G06F 17/15 (2006.01); G06F 111/02 (2020.01); G06F 111/20 (2020.01);
U.S. Cl.
CPC ...
G06F 30/20 (2020.01); G06F 9/4881 (2013.01); G06F 17/15 (2013.01); G06F 2111/02 (2020.01); G06F 2111/20 (2020.01);
Abstract

A GROMACS cloud computing process control method includes the following steps. Step (1): acquire crystal structures having energy ranked at the top by cluster ranking, and then obtain a GROMACS structural file. Step (2): acquire optimal force field parameters, and then automatically convert the optical force field parameters into a GROMACS force field parameter file. Step (3): import the yoda library function to automatically generate a corresponding GROMACS simulation parameter file according to simulation types in different stages. Step (4): import a mixc library function to submit GROMACS computation and analysis tasks to a task scheduling platform called Majorana. Step (5): add a corresponding monitor task for the same batch of tasks for each structure, and acquire the execution status of the tasks in real time. Step (6): acquire corresponding analysis results from ArangoDB after all the tasks are executed, and directly plot corresponding curves in Jupyter.


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