Yuan Qi, Santana-Bonilla Alejandro, Zwijnenburg Martijn A, Jelfs Kim E
Department of Chemistry, Molecular Sciences Research Hub, White City Campus, Imperial College London, Wood Lane, London, W12 0BZ, UK.
Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK.
Nanoscale. 2020 Mar 28;12(12):6744-6758. doi: 10.1039/c9nr10687a. Epub 2020 Mar 12.
As we seek to discover new functional materials, we need ways to explore the vast chemical space of precursor building blocks, not only generating large numbers of possible building blocks to investigate, but trying to find non-obvious options, that we might not suggest by chemical experience alone. Artificial intelligence techniques provide a possible avenue to generate large numbers of organic building blocks for functional materials, and can even do so from very small initial libraries of known building blocks. Specifically, we demonstrate the application of deep recurrent neural networks for the exploration of the chemical space of building blocks for a test case of donor-acceptor oligomers with specific electronic properties. The recurrent neural network learned how to produce novel donor-acceptor oligomers by trading off between selected atomic substitutions, such as halogenation or methylation, and molecular features such as the oligomer's size. The electronic and structural properties of the generated oligomers can be tuned by sampling from different subsets of the training database, which enabled us to enrich the library of donor-acceptors towards desired properties. We generated approximately 1700 new donor-acceptor oligomers with a recurrent neural network tuned to target oligomers with a HOMO-LUMO gap <2 eV and a dipole moment <2 Debye, which could have potential application in organic photovoltaics.
在我们寻求发现新型功能材料的过程中，我们需要方法来探索前驱体构建单元的广阔化学空间，不仅要生成大量可能的构建单元以供研究，还要尝试找到仅凭化学经验可能不会想到的非显而易见的选择。人工智能技术为生成大量用于功能材料的有机构建单元提供了一条可能的途径，甚至可以从非常小的已知构建单元初始库中做到这一点。具体而言，我们展示了深度循环神经网络在具有特定电子性质的供体-受体低聚物测试案例中对构建单元化学空间的探索应用。循环神经网络学会了如何通过在选定的原子取代（如卤化或甲基化）与分子特征（如低聚物的大小）之间进行权衡来生成新型供体-受体低聚物。通过从训练数据库的不同子集中进行采样，可以调整所生成低聚物的电子和结构性质，这使我们能够朝着所需性质丰富供体-受体库。我们用一个循环神经网络生成了大约1700种新的供体-受体低聚物，该网络被调整以靶向具有HOMO-LUMO能隙<2 eV和偶极矩<2德拜的低聚物，这些低聚物可能在有机光伏中有潜在应用。
