论文标题
通过量子机学习，几何放松和过渡状态搜索整个化学复合空间
Geometry Relaxation and Transition State Search throughout Chemical Compound Space with Quantum Machine Learning
论文作者
论文摘要
我们使用基于响应运算符的量子机学习（OQML）中预测的能量和力来执行使用传统优化器进行几何形状优化和过渡状态搜索计算。对于小型有机查询分子的随机采样初始坐标，我们报告了随着训练集尺寸的增加，平衡和过渡状态几何输出的系统改善。样本外S $ _ \ MATHRM {N} $ 2反应剂复合物和过渡状态几何形状已使用LBFGS和QST2算法进行了预测，RMSD为0.16和0.4Å，在培训多达200个反应群搜索率和过渡状态搜索轨迹之后。为了进行几何优化，我们还考虑了最多5'500宪法异构体的放松路径，具有总和c $ _7 $ H $ _ {10} $ o $ $ _2 $从qm9-database中。使用带有LBFGS优化器的结果OQML模型，以0.14〜Å的RMSD重现了最小几何形状。对于收敛的平衡和过渡状态几何，随后的振动正常模式频率分析表明，MP2参考结果平均分别为14和26 \，CM $^{ - 1} $。与DFT或MP2相比，OQML预测的数值成本可以忽略不计，但在任何一种情况下，直到收敛的步骤数通常都更大。然而，通过训练设定的大小，达到融合的成功率有系统地提高，强调了OQML的普遍适用性。
We use energies and forces predicted within response operator based quantum machine learning (OQML) to perform geometry optimization and transition state search calculations with legacy optimizers. For randomly sampled initial coordinates of small organic query molecules we report systematic improvement of equilibrium and transition state geometry output as training set sizes increase. Out-of-sample S$_\mathrm{N}$2 reactant complexes and transition state geometries have been predicted using the LBFGS and the QST2 algorithm with an RMSD of 0.16 and 0.4 Å -- after training on up to 200 reactant complexes relaxations and transition state search trajectories from the QMrxn20 data-set, respectively. For geometry optimizations, we have also considered relaxation paths up to 5'500 constitutional isomers with sum formula C$_7$H$_{10}$O$_2$ from the QM9-database. Using the resulting OQML models with an LBFGS optimizer reproduces the minimum geometry with an RMSD of 0.14~Å. For converged equilibrium and transition state geometries subsequent vibrational normal mode frequency analysis indicates deviation from MP2 reference results by on average 14 and 26\,cm$^{-1}$, respectively. While the numerical cost for OQML predictions is negligible in comparison to DFT or MP2, the number of steps until convergence is typically larger in either case. The success rate for reaching convergence, however, improves systematically with training set size, underscoring OQML's potential for universal applicability.
