机器学习加速结构搜索对联体保护集群的搜索
Lincan Fang1, Jarno Laakso1, Patrick Rinke1
1Department of Applied Physics, Aalto University, 00076 AALTO, Espoo, Finland.
The Journal of chemical physics
|March 1, 2024
概括
机器学习加速了在被联体保护的集群中寻找低能结构的搜索. 对于由氨酸配体保护的黄金集群,氨酸中的特定键影响结构和电子性质.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 确定由连接体保护的集群的低能结构是计算密集的.
- 庞大的结构空间对准确的结构预测构成了重大挑战.
研究的目的:
- 开发和应用一种机器学习方法,以加快对联体保护集群的低能结构的搜索.
- 调查由氨酸连接体保护的黄金集群的结构和电子特性.
主要方法:
- 使用基于内核刚性回归的机器学习方法.
- 采用Au25(Cys) 18集群作为方法验证的模型系统.
主要成果:
- 成功加速了对低能耗结构的搜索.
- 确定了氨酸中的特定键配置,作为低能结构的关键特征.
- 证明了连接体层的配置会影响集群属性.
结论:
- 机器学习方法对于预测联体保护集群的低能结构是有效的.
- 囊配体中的键在黄金集群的稳定性和结构中起着至关重要的作用.
- 连接物排列显著影响这些纳米材料的电子和结构特征.
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