Structure prediction and materials design with generative neural networks
Structure prediction and materials design with generative neural networks
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DOI：
10.1038/s43588-023-00471-w
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
D. Yan;Adam D. Smith;Cheng-Chien Chen
中科院分区：
文献类型：
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作者：
D. Yan;Adam D. Smith;Cheng-Chien Chen
稳定晶体结构的预测是设计具有所需性能的固态晶体材料的重要组成部分。结构特征表示和生成式神经网络的最新进展有望有效地创建用于逆向设计的新的稳定结构，并搜索具有定制功能的材料。
The prediction of stable crystal structures is an important part of designing solid-state crystalline materials with desired properties. Recent advances in structural feature representations and generative neural networks promise the ability to efficiently create new stable structures to use for inverse design and to search for materials with tailored functionalities.