{"id":6062,"date":"2025-07-18T09:53:00","date_gmt":"2025-07-18T00:53:00","guid":{"rendered":"https:\/\/aizoth.com\/?post_type=use_case&p=6062"},"modified":"2025-12-05T15:43:19","modified_gmt":"2025-12-05T06:43:19","slug":"contents-e012","status":"publish","type":"use_case","link":"https:\/\/aizoth.com\/en\/use_case\/contents-e012\/","title":{"rendered":"Exploration of next-generation solar cell materials using Multi-Sigma\u00ae"},"content":{"rendered":"\n

Using the AI analysis platform Multi-Sigma\u00ae<\/sup>, 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.<\/strong><\/p>\n\n\n\n

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1. Formation Energy and Bandgap Prediction<\/strong><\/h2>\n\n\n\n

Using Multi-Sigma\u00ae<\/sup><\/strong>\u2019s 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.<\/p>\n\n\n\n

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2. Contribution analysis<\/strong><\/h2>\n\n\n\n

Multi-Sigma\u00ae<\/sup><\/strong>‘s contribution analysis function identifies factors with positive and negative contributions. The figures on the right highlight key factors influencing formation energy and bandgap.<\/p>\n\n\n\n

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3. Multi-Objective Optimization<\/strong><\/h2>\n\n\n\n

Multi-Sigma\u00ae<\/sup><\/strong>‘s optimization function suggests the optimal parameter combinations to achieve target performance metrics.<\/p>\n\n\n\n