Exploration of
next-generation solar cell materials using Multi-Sigma

Using the AI analysis platform Multi-Sigma^®, we will construct a prediction model of formation energy and bandgap based on material descriptors. The models were linked for multi-objective optimization to identify compounds with promising properties for solar cell applications.

1. Formation Energy and Bandgap Prediction

Using Multi-Sigma’s prediction function, we trained models to map material descriptors to properties. Model 1 used electronegativity, period, group, density, and volume to predict formation energy, while Model 2 used electronegativity, period, group, and formation energy to predict the bandgap.

2. Factor analysis

Multi-Sigma’s factor analysis function identifies factors with positive and negative contributions. The figures on the right highlight key factors influencing formation energy and bandgap.

3. Multi-Objective Optimization

Multi-Sigma’s optimization function suggests the optimal parameter combinations to achieve target performance metrics.

• Minimization of formation energy (ensuring long-term stability)
• Achieve a bandgap of around 1.5 eV (optimal light absorption characteristics relative to the sunlight spectrum)

Multi-objective optimization with Multi-Sigma^® yielded a promising solar cell material candidate with the following physical properties.

Data source: https://next-gen.materialsproject.org/api