人工智能时代密度函数理论的核心作用
Bing Huang1, Guido Falk von Rudorff2,3, O Anatole von Lilienfeld4,5,6,7,8
1University of Vienna, Faculty of Physics, AT1090 Wien, Austria.
概括
密度函数理论 (DFT) 对材料科学至关重要. 最近由DFT数据驱动的机器学习 (ML) 模型显示出高效率和精度,使自动驾驶实验室的自动化实验设计成为可能.
科学领域:
- 计算化学和材料科学,专注于预测建模.
背景情况:
- 密度函数理论 (DFT) 是化学和材料科学的基石,因为它具有预测能力,效率和多功能性.
- 机器学习 (ML) 模型越来越多地利用 DFT 来生成合成数据和设计模型架构.
研究的目的:
- 审查使用DFT的ML模型的最新进展.
- 在化学和材料科学中对这些基于DFT的ML发展产生更广泛的影响.
主要方法:
- 对与DFT结合机器学习模型开发的最新文献的审查.
- 基于DFT的ML模型的性能指标 (效率,准确性,可扩展性,可转移性) 的分析.
主要成果:
- 基于DFT的ML模型在效率,准确性,可扩展性和可转移性方面取得了显著的改进.
- 这些进步使预测软件能够集成到自动化实验工作流程中.
结论:
- DFT和ML之间的协同作用正在改变化学和材料研究.
- 用DFT增强的ML模型正在为实验规划的自动驾驶实验室的常规使用铺平道路.
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