Prediction of Aqueous Stable Lead-Free Hybrid Halide Perovskites for Efficient Solar Water Splitting Using Machine Learning and Molecular Dynamics

Mahalaxmi M C; Madhana Gopal; Tumpa Sadhukhan; Athira Nambiar; Meenal Deo

doi:10.1039/d6tc00296j

January 1, 2026Journal of Materials Chemistry COpen Access

Prediction of Aqueous Stable Lead-Free Hybrid Halide Perovskites for Efficient Solar Water Splitting Using Machine Learning and Molecular Dynamics

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Key Points

The aim is to predict stable lead-free hybrid halide perovskites for efficient solar water splitting using advanced computational methods.
Utilized machine learning algorithms to model and predict properties of hybrid perovskites.
Employed molecular dynamics simulations to assess stability and performance.
Analyzed the electronic properties and bandgaps of various compositions.
Identified several stable lead-free perovskite structures.
Demonstrated improved efficiency in solar water splitting compared to traditional materials.
Showed that machine learning can successfully predict desirable properties in perovskites.

Implication

This research demonstrates the potential of machine learning to enhance solar water splitting efficiency in hybrid perovskites, suggesting promising applications in clean energy.

Abstract

Hybrid organic-inorganic halide perovskites (HOIP), have garnered significant attention in many opto-electronic applications due to their high efficiency and tuneable bandgaps. However, their application in the field of solar water...

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Cite This Study

C et al. (2026) studied this question.

synapsesocial.com/papers/69cd7a4e5652765b073a7648 https://doi.org/10.1039/d6tc00296j

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Prediction of Aqueous Stable Lead-Free Hybrid Halide Perovskites for Efficient Solar Water Splitting Using Machine Learning and Molecular Dynamics

Key Points

Implication

Abstract

Discussion

Cite This Study

Prediction of Aqueous Stable Lead-Free Hybrid Halide Perovskites for Efficient Solar Water Splitting Using Machine Learning and Molecular Dynamics

Key Points

Implication

Abstract

Discussion

Cite This Study

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