贝叶斯优化用于有效预测纳米孔材料中的气体吸收
1Advanced Materials Laboratory, CSIR-Central Leather Research Institute, Sardar Patel Road, Adyar, Chennai, 600020, India.
概括
贝叶斯优化 (BO) 加快了用于储存气体的纳米孔性材料 (NPM) 的发现. 有预期改进的高斯过程模型有效指导材料优化,降低气候和能源解决方案的计算成本.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 纳米孔材料 (NPMs),包括金属有机框架 (MOFs) 和共价有机框架 (COFs),对于应对全球挑战至关重要.
- 传统的NPM优化的实验方法是缓慢而昂贵的.
- 需要有效的策略来加快NPM的发现和设计,用于诸如气体储存等应用.
研究的目的:
- 介绍一个贝叶斯优化 (BO) 策略,以有效地导航NPM设计空间.
- 在MOF优化中对BO的各种代用模型和获取函数组合进行定量评估.
- 确定最佳的机器学习模型,用于在BO中作为材料发现的替代品.
主要方法:
- 在MOF数据集上使用机器学习 (ML) 技术进行回归分析.
- 评估多个ML模型,选择前三个准确模型作为替代品.
- 使用高斯过程 (GP) 作为替代模型,并将预期改进 (EI) 作为获取函数.
主要成果:
- 有预期改善 (EI) 的高斯过程 (GP) 并且没有马先验,优于其他替代模型.
- 对于预测来说,表现最好的ML模型可能不是对BO最合适的替代品.
- BO显著减少了对GCMC和DFT等计算密集型方法的依赖.
结论:
- 贝叶斯优化提供了一个有效的框架来加速NPM的发现和优化.
- 替代模型和获取功能的选择对于材料科学中有效的BO至关重要.
- 这种方法提高了计算效率,有助于储能和环境可持续性的努力.
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