论文标题
在化学空间中对不稳定分子进行故障排除
Troubleshooting Unstable Molecules in Chemical Space
论文作者
论文摘要
自动化化合物空间探索的一个主要挑战是确保最小能量几何形状的准确性 - - 以保留预期的键合连接。我们讨论了一个迭代的高通量工作流程，以保存连接性的几何优化，以利用量子机械模型之间的近距离。该方法在QM9数据集上进行了基准测试，其中包括133,885个小分子的DFT级特性。其中3,054个具有可疑的几何稳定性。我们成功地对2,988个分子进行了故障排除，并确保所需的刘易斯公式与最终几何形状之间进行射击。我们的工作流程基于DFT和DFT方法，将66个分子识别为不稳定。 52包含$ - {\ rm nno} - $，其余的由于金字塔sp $^2 $ C的紧张。在策划数据集中，我们检查具有长CC键的分子并确定超级参赛者（$ r> 1.70 $ 〜〜1.70 $ 〜〜）。我们希望提出的策略在大数据量子化学计划中发挥作用。
A key challenge in automated chemical compound space explorations is ensuring veracity in minimum energy geometries---to preserve intended bonding connectivities. We discuss an iterative high-throughput workflow for connectivity preserving geometry optimizations exploiting the nearness between quantum mechanical models. The methodology is benchmarked on the QM9 dataset comprising DFT-level properties of 133,885 small molecules; of which 3,054 have questionable geometric stability. We successfully troubleshoot 2,988 molecules and ensure a bijective mapping between desired Lewis formulae and final geometries. Our workflow, based on DFT and post-DFT methods, identifies 66 molecules as unstable; 52 contain $-{\rm NNO}-$, the rest are strained due to pyramidal sp$^2$ C. In the curated dataset, we inspect molecules with long CC bonds and identify ultralong contestants ($r>1.70$~Å) supported by topological analysis of electron density. We hope the proposed strategy to play a role in big data quantum chemistry initiatives.
