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

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.