Advancing our Understanding of Optoionic Effects for the Design of Solar Batteries: A Theoretical Perspective

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 batteriesand 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 these processes, particularly when the 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.

Publication Details

Journal
ArXiv.org
Published
2025-03-10
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

Advancing our Understanding of Optoionic Effects for the Design of Solar Batteries: A Theoretical Perspective

ArXiv.org
Machine Learning in Materials Science
article

Advancing our Understanding of Optoionic Effects for the Design of Solar Batteries: A Theoretical Perspective

article en

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 batteriesand 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 these processes, particularly when the 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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Machine Learning in Materials Science
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Advancing our Understanding of Optoionic Effects for the Design of Solar Batteries: A Theoretical Perspective · ArXiv.org (2025) | TGRS Research Map | TGRS