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Updated: Feb 1, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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通过使用结构描述符的多步机器学习来预测2D材料中的负热膨胀

Arko Mohari1, Soumya Mondal1, Debashis Sing Mura1

  • 1School of Chemical Sciences, Indian Association for the Cultivation of Science, Kolkata, West Bengal, India.

Chemistry, an Asian journal
|January 31, 2026
PubMed
概括

本研究引入了一种机器学习模型,有效地预测2D材料的负热膨胀 (NTE). 该模型准确地识别了194种新的NTE材料,加速了先进的热膨胀材料的发现.

关键词:
两维材料是二维材料.人工神经网络的人工神经网络机器学习是机器学习.超材料是指金属材料.负热膨胀是指负的热膨胀.几乎和的近似方法.

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