通过卷积神经网络对团的电子密度映射来增强结构预测算法
Pinaki Saha1, Minh Tho Nguyen2,3
1School of Physics, Engineering and Computer Science, University of Hertfordshire UK.
RSC advances
|October 23, 2023
概括
我们开发了一个卷积神经网络模型来预测纳米集群能量,加速材料研究. 这种人工智能方法有助于比传统方法更有效地确定原子集群结构.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 纳米技术 纳米技术
背景情况:
- 原子集群结构决定了纳米集群属性,这对于材料研究至关重要.
- 传统的量子力学 (QM) 计算用于结构阐明,对于大型纳米集群来说,计算密集且耗时.
- 现有的结构预测算法仍然依赖于QM进行评估,增加计算成本.
研究的目的:
- 开发一个计算效率高的模型来预测纳米集群能量.
- 创建一个工具,以帮助加快纳米集群的结构预测过程.
- 为了在材料研究中更快地探索原子集群结构.
主要方法:
- 开发一个卷积神经网络 (CNN) 模型.
- 利用促分子密度进行飞行式能量预测.
- 在各种尺寸的纯纳米集的数据集上测试CNN模型.
主要成果:
- CNN模型为纳米集群的基本状态提供了相对准确的能量.
- 该模型展示了与现有的结构预测算法集成的潜力.
- 在纯纳米集群上成功应用表明可通用性.
结论:
- 开发的CNN模型为纳米集群能量预测提供了一个计算效率高的替代方案.
- 这种人工智能驱动的方法可以显著加速新材料的发现和设计.
- 该模型有助于克服传统QM计算的局限性,探索大型纳米集群结构.
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