🌞🤖 Machine Learning for Predicting Chalcohalide Energy Band Gaps: A Pathway to Next-Generation Solar Materials
Chalcohalides are an emerging class of semiconductor materials that combine chalcogen elements (such as sulfur, selenium, or tellurium) with halogens (like chlorine, bromine, or iodine). These hybrid compounds possess fascinating optical and electronic properties, making them promising candidates for photovoltaic devices, photodetectors, and optoelectronic technologies . However, discovering suitable chalcohalide materials with optimal energy band gaps through conventional experimental methods is time-consuming and expensive. This challenge has motivated researchers to adopt Machine Learning (ML) as a powerful predictive tool. 🚀 🧠 Role of Machine Learning in Materials Discovery Machine Learning enables scientists to analyze massive datasets and identify hidden patterns within complex material structures. By training ML models with known experimental and computational data, researchers can predict the band gap values of unexplored chalcohalide compounds with remarkable accu...