tmQM Dataset-Quantum Geometries and Properties of 86k Transition Metal Complexes.
tmQM Dataset-Quantum Geometries and Properties of 86k Transition Metal Complexes.
复制标题
DOI：
10.1021/acs.jcim.0c01041
复制
发表时间：
2020-12-28
影响因子：
5.6
通讯作者：
Skjelstad BB
中科院分区：
文献类型：
--
作者：
Balcells D;Skjelstad BB
我们报道了过渡金属量子力学(TmQM)数据集，它包含了一个大的过渡金属-有机化合物空间的几何和性质。TmQM由从剑桥结构数据库中提取的86,665个单核配合物组成，包括Werner、生物无机和基于多种有机配体的有机金属配合物和30种过渡金属(第3至12族中的3d、4d和5d)。所有配合物都是闭壳结构，形式电荷在{+1，0，−1}e范围内。tmQM数据集提供了在GFN2XTB水平上优化的所有金属配合物的笛卡尔坐标，以及它们的分子尺寸、化学计量和金属节点度。在密度泛函DFT(TPSSh-D3BJ/Def2-SVP)水平上计算了体系的电子能和色散能、最高占据分子轨道(HOMO)和最低空分子轨道(LUMO)能、HOMO/LUMO能级、偶极矩和金属中心的自然电荷，并给出了GFN_2-XTB的极化率。成对表示显示了这些性质之间的低相关性，提供了具有不寻常化学空间区域的几乎连续的图，例如，结合了大的极化率与宽的HOMO/LUMO能隙的络合物，以及结合了低能HOMO轨道与富含电子的金属中心的络合物。TmQM数据集可以用于数据驱动的新金属络合物发现，包括基于机器学习的预测模型。这些模型可能会对过渡金属化学发挥关键作用的领域产生重大影响，例如催化、有机合成和材料科学。TmQM是一个开放的数据集，可以从免费下载。
We report the transition metal quantum mechanics (tmQM) data set, which contains the geometries and properties of a large transition metal–organic compound space. tmQM comprises 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic, and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d, and 5d from groups 3 to 12). All complexes are closed-shell, with a formal charge in the range {+1, 0, −1}e. The tmQM data set provides the Cartesian coordinates of all metal complexes optimized at the GFN2-xTB level, and their molecular size, stoichiometry, and metal node degree. The quantum properties were computed at the DFT(TPSSh-D3BJ/def2-SVP) level and include the electronic and dispersion energies, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, HOMO/LUMO gap, dipole moment, and natural charge of the metal center; GFN2-xTB polarizabilities are also provided. Pairwise representations showed the low correlation between these properties, providing nearly continuous maps with unusual regions of the chemical space, for example, complexes combining large polarizabilities with wide HOMO/LUMO gaps and complexes combining low-energy HOMO orbitals with electron-rich metal centers. The tmQM data set can be exploited in the data-driven discovery of new metal complexes, including predictive models based on machine learning. These models may have a strong impact on the fields in which transition metal chemistry plays a key role, for example, catalysis, organic synthesis, and materials science. tmQM is an open data set that can be downloaded free of charge from .
登录
查看更多内容
影响因子：
8.4
作者：
Friederich P;Dos Passos Gomes G;De Bin R;Aspuru-Guzik A;Balcells D
通讯作者：
Balcells D
影响因子：
13.6
作者：
Bartók AP;De S;Poelking C;Bernstein N;Kermode JR;Csányi G;Ceriotti M
通讯作者：
Ceriotti M
在化学空间中丢失？支持有机金属催化的地图。
影响因子：
--
作者：
Fey N
通讯作者：
Fey N
影响因子：
18.2
作者：
Gómez-Bombarelli R;Wei JN;Duvenaud D;Hernández-Lobato JM;Sánchez-Lengeling B;Sheberla D;Aguilera-Iparraguirre J;Hirzel TD;Adams RP;Aspuru-Guzik A
通讯作者：
Aspuru-Guzik A
影响因子：
--
作者：
Baumes, L. A.;Serra, J. M.;Corma, A.
通讯作者：
Corma, A.