Advancing our understanding of optoionic effects for the design of solar batteries: a theoretical perspective

Abstract Optoionics, a promising new field that aims at controlling ion dynamics using light, links photovoltaic power generation with electrochemical charge storage. This has the potential to drive and accelerate the energy revolution by utilizing materials that integrate the functionality of batteries and photovoltaic cells. Finding, optimizing, and customizing these materials is a complex task, though. Computational modeling can play a crucial role in guiding and speeding up such developments, particularly when the acting atomic mechanisms are not well understood. This does however require expertise in various areas, including advanced electronic-structure theory, machine learning, and multi-scale approaches. In this perspective, we shed light on the intricacies of modeling optoionic effects for solar battery materials. We first discuss the underlying physical and chemical mechanisms, as well as the computational tools that are available to date for describing these processes. Furthermore, we discuss the limits of these approaches and identify key challenges that need to be tackled to advance this field.

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Publication Details

Journal
Journal of Physics Materials
Published
2025-07-01
DOI
https://doi.org/10.1088/2515-7639/adeaec
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Advancing our understanding of optoionic effects for the design of solar batteries: a theoretical perspective

Karsten Reuter, Christian Carbogno, Matteo Rinaldi, Matthias Kick
Journal of Physics Materials
Machine Learning in Materials Science
article

Advancing our understanding of optoionic effects for the design of solar batteries: a theoretical perspective

Karsten Reuter, Christian Carbogno, Matteo Rinaldi, Matthias Kick
article en

Abstract

Abstract Optoionics, a promising new field that aims at controlling ion dynamics using light, links photovoltaic power generation with electrochemical charge storage. This has the potential to drive and accelerate the energy revolution by utilizing materials that integrate the functionality of batteries and photovoltaic cells. Finding, optimizing, and customizing these materials is a complex task, though. Computational modeling can play a crucial role in guiding and speeding up such developments, particularly when the acting atomic mechanisms are not well understood. This does however require expertise in various areas, including advanced electronic-structure theory, machine learning, and multi-scale approaches. In this perspective, we shed light on the intricacies of modeling optoionic effects for solar battery materials. We first discuss the underlying physical and chemical mechanisms, as well as the computational tools that are available to date for describing these processes. Furthermore, we discuss the limits of these approaches and identify key challenges that need to be tackled to advance this field.

Journal of Physics MaterialsVol. 8(3)
Fritz Haber Institute of the Max Planck Society (DE)
Max-Planck-Förderstiftung
Openalex Percentile: Top 89%
Machine Learning in Materials Science
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Advancing our understanding of optoionic effects for the design of solar batteries: a theoretical perspective — Karsten Reuter, Christian Carbogno, et al. · Journal of Physics Materials (2025) | TGRS Research Map | TGRS