{"title":"pydiffusion:一个用于扩散模拟和数据分析的Python库","authors":"Zhangqi Chen, Qiaofu Zhang, Ji-Cheng Zhao","doi":"10.5334/jors.255","DOIUrl":null,"url":null,"abstract":"pydiffusion is a free and open-source Python library designed to solve diffusion problems for both single-phase and multi-phase binary systems. The key features of pydiffusion include fast simulation of multi-phase diffusion and extraction of diffusion coefficients from experimental concentration profiles using forward simulation analysis. pydiffusion also provides various mathematical models for diffusion profile smoothing, diffusion coefficient evaluation, and data optimization. In pydiffusion, diffusion profiles and various phases are easy to define or read from the experimental datasets. Visualization tools based on Matplotlib are also provided to help users present or refine their simulations and analysis. Funding statement: The development of pydiffusion is supported by the US National Science Foundation (NSF) under Grant number CMMI-1333999, and it is part of an NSF Designing Materials to Revolutionize and Engineer our Future (DMREF) project.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"pydiffusion: A Python Library for Diffusion Simulation and Data Analysis\",\"authors\":\"Zhangqi Chen, Qiaofu Zhang, Ji-Cheng Zhao\",\"doi\":\"10.5334/jors.255\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"pydiffusion is a free and open-source Python library designed to solve diffusion problems for both single-phase and multi-phase binary systems. The key features of pydiffusion include fast simulation of multi-phase diffusion and extraction of diffusion coefficients from experimental concentration profiles using forward simulation analysis. pydiffusion also provides various mathematical models for diffusion profile smoothing, diffusion coefficient evaluation, and data optimization. In pydiffusion, diffusion profiles and various phases are easy to define or read from the experimental datasets. Visualization tools based on Matplotlib are also provided to help users present or refine their simulations and analysis. Funding statement: The development of pydiffusion is supported by the US National Science Foundation (NSF) under Grant number CMMI-1333999, and it is part of an NSF Designing Materials to Revolutionize and Engineer our Future (DMREF) project.\",\"PeriodicalId\":37323,\"journal\":{\"name\":\"Journal of Open Research Software\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Open Research Software\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5334/jors.255\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Open Research Software","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5334/jors.255","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
pydiffusion: A Python Library for Diffusion Simulation and Data Analysis
pydiffusion is a free and open-source Python library designed to solve diffusion problems for both single-phase and multi-phase binary systems. The key features of pydiffusion include fast simulation of multi-phase diffusion and extraction of diffusion coefficients from experimental concentration profiles using forward simulation analysis. pydiffusion also provides various mathematical models for diffusion profile smoothing, diffusion coefficient evaluation, and data optimization. In pydiffusion, diffusion profiles and various phases are easy to define or read from the experimental datasets. Visualization tools based on Matplotlib are also provided to help users present or refine their simulations and analysis. Funding statement: The development of pydiffusion is supported by the US National Science Foundation (NSF) under Grant number CMMI-1333999, and it is part of an NSF Designing Materials to Revolutionize and Engineer our Future (DMREF) project.