在金属有机框架中探索储能能力:贝叶斯式优化方法
Sumedh Ghude1, Chandra Chowdhury2
1Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India.
Chemistry (Weinheim an der Bergstrasse, Germany)
|August 28, 2023
概括
贝叶斯优化 (BO) 有效地选用于 (H2) 储存的金属有机框架 (MOF),以最小的计算确定顶级候选者. 这种人工智能方法显著减少了材料发现的实验力度.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 金属有机框架 (MOF) 对于气体储存,捕获和传感至关重要.
- 大型MOF数据库的高吞吐量选,以获得最佳吸附性能,在计算上是昂贵的.
- 需要有效的方法来确定气体分离和储存应用的有希望的MOF.
研究的目的:
- 证明贝叶斯优化 (BO) 对于估计MOF中的H2吸收的有效性.
- 显著降低选大型MOF数据库的计算成本.
- 为优化MOF属性和材料设计见解提供一个框架.
主要方法:
- 利用现有的98,000个真实和假设的MOF数据集.
- 应用贝叶斯优化 (BO) 来预测H2吸收能力.
- 将BO与粒子群优化 (PSO) 和一种新的进化PSO (EPSO) 变体进行比较.
主要成果:
- 通过对数据库中不到0.027%的数据进行选,BO确定了顶级候选MOF.
- 该BO方法显著降低了MOF查所需的实验力度.
- 公共服务局和EPSO在估计H2吸收潜力方面取得了与BO相似的结果.
结论:
- 贝叶斯优化是一种高效的工具,可以加速发现用于H2存储的MOF.
- 开发的框架为优化各种材料性能提供了可转移的方法.
- 像BO和PSO这样的人工智能驱动的方法为材料科学提供了强大的替代品,而不是传统的计算选.
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