Liang Jiechun, Ye Shuqian, Dai Tianshu, Zha Ziyue, Gao Yuechen, Zhu Xi
Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), 13-15F, Tower G2, Xinghe World, Rd Yabao, Longgang District, Shenzhen, Guangdong, 518172, China.
Department of Mathematics, College of Letters and Science, University of California, Santa Barbara 522 University RD, Santa Barbara, CA, 93106-3080, USA.
Sci Data. 2020 Nov 18;7(1):400. doi: 10.1038/s41597-020-00746-1.
In the research field of material science, quantum chemistry database plays an indispensable role in determining the structure and properties of new material molecules and in deep learning in this field. A new quantum chemistry database, the QM-sym, has been set up in our previous work. The QM-sym is an open-access database focusing on transition states, energy, and orbital symmetry. In this work, we put forward the QM-symex with 173-kilo molecules. Each organic molecular in the QM-symex combines with the Ch symmetry composite and contains the information of the first ten singlet and triplet transitions, including energy, wavelength, orbital symmetry, oscillator strength, and other quasi-molecular properties. QM-symex serves as a benchmark for quantum chemical machine learning models that can be effectively used to train new models of excited states in the quantum chemistry region as well as contribute to further development of the green energy revolution and materials discovery.
在材料科学研究领域,量子化学数据库在确定新材料分子的结构和性质以及该领域的深度学习中发挥着不可或缺的作用。我们在之前的工作中建立了一个新的量子化学数据库——QM-sym。QM-sym是一个开放获取的数据库,专注于过渡态、能量和轨道对称性。在这项工作中,我们提出了包含17.3万个分子的QM-symex。QM-symex中的每个有机分子都与Ch对称性复合物结合,并包含前十种单重态和三重态跃迁的信息,包括能量、波长、轨道对称性、振子强度和其他准分子性质。QM-symex作为量子化学机器学习模型的基准,可有效用于训练量子化学区域激发态的新模型,也有助于绿色能源革命和材料发现的进一步发展。