对发现有机太阳能电池供体分子的搜索方法的比较分析
Mohammed Azzouzi1,2, Steven Bennett1, Victor Posligua1
1Department of Chemistry, Imperial College London White City Campus W12 0BZ London UK K.jelfs@imperial.ac.uk.
概括
对于有机光伏来说,探索广的化学空间是一项挑战. 像贝叶斯优化这样的先进搜索算法在识别有前途的OPV分子方面显著优于随机搜索.
科学领域:
- 材料科学
- 计算化学
- 有机电子
背景情况:
- 有机光伏 (OPV) 等应用的新型有机分子的识别受到化学空间的巨大阻碍.
- 传统的物业评估方法往往耗时,资源密集,限制了勘探.
- 由单体构建块构建的大小寡合体代表了这种化学空间的重要部分.
研究的目的:
- 开发和评估用于OPV应用的大型寡合体化学空间的计算搜索策略.
- 比较各种搜索算法的性能,包括贝叶斯优化和进化方法,以识别具有可取性质的分子.
- 在极大的分子数据集中展示这些搜索方法的可扩展性和有效性.
主要方法:
- 开发一个Python包",stk-search",使用化学空间探索的构建块方法.
- 多种搜索算法的实现和比较:贝叶斯优化,进化算法和随机搜索.
- 在预先计算的搜索空间和6mers (超过10^14分子) 的更大的化学空间中对算法性能进行评估.
主要成果:
- 搜索算法在小,预先计算的化学空间中比随机搜索有所改善.
- 在6mers的大化学空间中,贝叶斯优化表现出卓越的性能,识别出比随机搜索更有前途的分子数量级.
- 与在相同的计算约束下随机搜索相比,贝叶斯优化实现了识别可取的OPV分子的千倍增长.
结论:
- 计算搜索策略,特别是贝叶斯优化,对于有机电子在大型化学空间的高效发现至关重要.
- "stk-search"套件为加速识别有机光伏的高性能材料提供了一个强大的工具.
- 先进的搜索算法比随机探索具有显著的优势,能够快速识别具有所需属性的目标分子.
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