均等神经网络利用分子集群进行准确的分子晶格能量预测
Ankur K Gupta1, Miko M Stulajter1, Yusuf Shaidu2,3
1Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.
ACS omega
|September 30, 2024
概括
像Allegro这样的等价神经网络可以有效地预测分子晶体结构的能量. 这种方法降低了计算成本,并加速了低能水晶结构的发现.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 机器学习 机器学习
背景情况:
- 同等变量神经网络为原子间潜力提供高数据效率和概括性.
- 传统的密度函数理论 (DFT) 用于预测晶体结构的方法在计算上昂贵.
- 预测有机分子晶体结构需要准确和高效的方法.
研究的目的:
- 适应等价神经网络来预测分子晶体结构能量.
- 为了降低与晶体结构属性计算相关的计算成本.
- 为了增强低能晶体结构的发现.
主要方法:
- 使用了Allegro等价神经网络架构.
- 通过基于高斯式轨道 (GTO) 的方法对分子集群进行了网络训练.
- 将受过训练的Allegro网络与USPEX晶体结构预测框架集成.
主要成果:
- 以降低计算成本实现了分子晶体结构能量的准确预测.
- 证明了网络的预测与平面波DFT结果密切一致.
- 通过框架集成成功加速了低能晶体结构的发现.
结论:
- 同等变量神经网络为预测分子晶体特性提供了一个计算效率高,准确的替代方案.
- 这种方法绕过了需要资源密集的定期DFT计算的需求.
- 与USPEX集成显著加快了晶体结构预测工作流程.
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