推引擎驱动的新稳定化合物的广泛预测
Sean D Griesemer1,2, Bianca Baldassarri1, Ruijie Zhu1
1Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA.
Science advances
|January 3, 2025
概括
稳定无机化合物的高通量计算搜索由改进的推引擎,特别是神经网络加速,识别成千上万的新材料.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 固态物理 固态物理
背景情况:
- 使用密度函数理论 (DFT) 的高通量计算搜索加速了新无机化合物的发现.
- 尽管取得了进展,但DFT的庞大搜索空间和计算成本限制了稳定化合物发现的效率.
- 推引擎已经开发出来,以更有效地指导这些搜索.
研究的目的:
- 系统地比较现有的推引擎的性能,以发现稳定的无机化合物.
- 为了确定这些推引擎的改进.
- 发现大量新的稳定无机化合物并探索它们的潜在应用.
主要方法:
- 基于元素替代,数据挖掘和神经网络预测形成的推引擎的比较分析.
- 开发和应用改进的推引擎.
- 使用计算方法在零温度和压力下确定稳定的化合物.
主要成果:
- 基于神经网络的推引擎在识别稳定的Heusler化合物方面表现出卓越的性能.
- 发现了成千上万种新的稳定无机化合物.
- 已识别的化合物包括难以捉摸的混合离子化合物,现在可以在开放量子材料数据库中获得.
结论:
- 改进的推引擎,特别是神经网络,显著提高了发现稳定无机化合物的效率.
- 新发现的化合物有可能在热电和太阳能热化学燃料生产等领域应用.
- 开放量子材料数据库被扩展为一组多样化的新型稳定化合物.
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