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Assessing Data-Driven Predictions of Band Gap and Electrical Conductivity for Transparent Conducting Materials

💡 原文英文，约100词，阅读约需1分钟。

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内容提要

本文探讨了机器学习在发现新型透明导电材料（TCMs）时面临的数据数量和质量不足的问题。研究提出了一种数据驱动的框架，通过建立实验数据库和评估机器学习模型，推动新材料的发现。结果表明，机器学习能够识别相似材料并挖掘被忽视的潜在材料。

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