Exploring different search approaches to discover donor molecules for organic solar cells

Identifying organic molecules with desirable properties from the extensive chemical space can be challenging, particularly when property evaluation methods are time-consuming and resource intensive. In this study, we illustrate this challenge by exploring the chemical space of large oligomers, constructed from monomeric building blocks, for potential use in organic photovoltaics (OPV). To facilitate this exploration, we developed a Python package called stk-search, which employs a building block approach. For this purpose, we developed a python package to search the chemical space using a building block approach: stk-search. We use stk-search (GitHub link) to compare a variety of search algorithms, including those based upon Bayesian optimization and evolutionary approaches. Initially, we evaluated and compared the performance of different search algorithms within a precomputed search space. We then extended our investigation to the vast chemical space of molecules formed of 6 building blocks (6-mers), comprising over 10¹⁴ molecules. Notably, while some algorithms show only marginal improvements over a random search approach in a relatively small, precomputed, search space, their performance in the larger chemical space is orders of magnitude better. Specifically, Bayesian optimization identified a thousand times more promising molecules with the desired properties compared to random search, using the same computational resources.
This record contains the dataset generated during the exploration of the space of molecules formed of 6 building blocks for application as donor molecules for OPV application, with calculated properties such as Ionisation potential, excited state energy and oscillator strength.

从庞大的化学空间中识别具有理想特性的有机分子是一项颇具挑战性的任务，尤其是当属性评估方法耗时且资源密集时。在本研究中，我们通过探讨由单体构建单元构成的大寡聚物化学空间，以及其在有机光伏（OPV）领域的潜在应用，来阐述这一挑战。为了便于这一探索，我们开发了一个名为 stk-search 的 Python 包，该包采用构建块方法。为此，我们开发了一个 Python 包，即 stk-search，以构建块方法搜索化学空间。我们使用 stk-search（GitHub链接）来比较各种搜索算法，包括基于贝叶斯优化和进化方法的算法。最初，我们在预计算的搜索空间内评估和比较了不同搜索算法的性能。随后，我们将研究扩展到由6个构建块（6-聚体）组成的庞大化学空间，这些6-聚体包含超过10¹⁴个分子。值得注意的是，虽然一些算法在相对较小的预计算搜索空间中相对于随机搜索方法仅显示出微小的改进，但它们在更大化学空间中的性能却好出数个数量级。具体而言，贝叶斯优化在相同的计算资源下，相较于随机搜索，识别出具有所需特性的分子数量增加了千倍。本记录包含在探索由6个构建块组成的分子空间时生成的数据集，这些分子作为OPV应用的供体分子，并计算了如电离势、激发态能量和振子强度等属性。