An algorithmic looking glass for transitions

IF 38.1 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Nature reviews. Chemistry Pub Date : 2024-10-23 DOI:10.1038/s41570-024-00667-2

Alexander Rosu-Finsen

引用次数: 0

Abstract

Glass transition temperatures are determined through, for instance, calorimetry, but maybe machine learning models can predict them. Here, researchers test this idea with published data as input for the model, to find a close correlation between predicted and experimental values.

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过渡时期的算法观察镜

玻璃转化温度可以通过量热法等方法确定，但机器学习模型或许可以预测玻璃转化温度。在这里，研究人员利用已公布的数据作为模型的输入，对这一想法进行了测试，发现预测值与实验值之间存在密切的相关性；

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature reviews. Chemistry Chemical Engineering-General Chemical Engineering

CiteScore

52.80

自引率

0.80%

发文量

期刊介绍： Nature Reviews Chemistry is an online-only journal that publishes Reviews, Perspectives, and Comments on various disciplines within chemistry. The Reviews aim to offer balanced and objective analyses of selected topics, providing clear descriptions of relevant scientific literature. The content is designed to be accessible to recent graduates in any chemistry-related discipline while also offering insights for principal investigators and industry-based research scientists. Additionally, Reviews should provide the authors' perspectives on future directions and opinions regarding the major challenges faced by researchers in the field.