pyscses is a Python package for modelling ionic space-charges in solid electrolytes. Its
primary use is to calculate equilibrium distributions of point-charge atomic defects within
one-dimensional “Poisson-Boltzmann”-like mean-field models. These calculations take as
inputs a set of defect site positions, within a specific crystal structure, and the associated defect segregation energies. pyscses can also be used to calculate ionic transport
properties (space-charge resistivities and activation energies) for these equilibrium defect
One approach to modelling space-charge formation in solid electrolytes is to consider
defects as ideal point-charges embedded in a continuum dielectric, and to calculate equilibrium defect distributions by solving mean-field “Poisson-Boltzmann”-like equations
(Franceschetti, 1981; Guo & Waser, 2006; E. E. Helgee, Lindman, & Wahnström, 2013;
A. Lindman, Helgee, & Wahnström, 2013; Nyman, Helgee, & Wahnström, 2012; Polfus, Norby, & Bredesen, 2016). While numerical solutions to the 1D Poisson-Boltzmann
equation are relatively simple to implement, published results are typically obtained using
private closed-source codes, making it difficult to reproduce results or to test the effect
of different approximations included in specific models. pyscses provides an open-source
Python package for modelling space-charge formation in solid electrolytes, within a 1D
Poisson-Boltzmann-like formalism.We are currently using pyscses in our own research
into space-charge formation in solid electrolytes for fuel cells and lithium-ion batteries,
and hope that this open-source resource will support reproducible resear
Original languageEnglish
Article number1209
JournalThe Journal of Open Source Software
Issue number35
Publication statusPublished - 20 Mar 2019


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