机器学习用于密度函数近似的准确性
1SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, California, USA.
Journal of computational chemistry
|April 26, 2024
概括
机器学习通过加速模拟和提高密度函数理论准确性来增强计算化学. 研究人员正在探索可转移模型,以便在化学和材料科学中得到更广泛的应用.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 在计算化学中越来越重要.
- ML加速了原子学模拟,并有助于材料设计.
- 机器学习可以提高电子结构方法的准确性,例如密度函数理论 (DFT).
研究的目的:
- 审查最近应用ML以提高DFT准确性的进展.
- 讨论创建可转移的ML模型的潜力和挑战.
- 检查ML模型在训练数据之外的系统上的性能.
主要方法:
- 关于计算机化学中的ML最近文献的综述.
- 对密度函数近似应用的ML模型的分析.
- 关于ML模型在不同的化学系统中可转移的案例研究.
主要成果:
- ML在纠正DFT的基本错误方面表现有前途.
- 最近在提高DFT准确性方面取得了进展.
- 机器学习模型在各种化学学科的可转移性仍然是一个挑战.
结论:
- 机器学习是推进计算化学和材料设计的强大工具.
- 开发广泛适用和可转移的ML模型至关重要.
- 需要进一步的研究来克服ML模型概括的挑战.
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