Enhancing synthesis prediction via machine learning

被引：0
|
作者：
Schoen, J. C. [1 ]
机构：
[1] MPI Solid State Res, Stuttgart, Germany
来源：
NATURE COMPUTATIONAL SCIENCE | 2025年 / 5卷 / 02期
关键词：
D O I：
10.1038/s43588-025-00771-3
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Identifying promising synthesis targets and designing routes to their synthesis is a grand challenge in chemistry and materials science. Recent work employing machine learning in combination with traditional approaches is opening new ways to address this truly Herculean task. © Springer Nature America, Inc. 2025.
引用
收藏
页码：95 / 96
页数：2
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