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

页码：95 / 96

页数：2

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