QM-symex, update of the QM-sym database with excited state information for 173 kilo molecules

Abstract

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 Cnh 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.

Molecular excited states through a machine learning lens

“…Recently, another dataset QM-symex with excited-state properties for ten excitations of 173,000 symmetric molecules was reported. 101 As a solution to the limited data availability, ML models are often developed and trained on much more computationally affordable bandgaps or orbital energies from which bandgaps can be calculated. 85,95,98,[102][103][104][105][106][107][108][109][110][111][112][113][114][115] Such studies are also facilitated by the availability of many big databases with these properties.…”

Machine learned calibrations to high-throughput molecular excited state calculations

“…First, we require large chemical diversity in our training set, and most of the existing molecular excited state databases use TD-DFT. 24,[37][38][39][40] The largest databases, namely PubChemQC 38 and QM-symex, 41 use B3LYP. Second, B3LYP was used in previous works using linearly calibrated xTB-sTDA 15,16 and is used extensively in machine learning and high-throughput screening studies.…”

“…Specifically, the training sets for the ML models considered in this study were derived from the existing PubChemQC (PCQC) 38 and QM-symex 40 databases. For concision, we will use the (functional/basis set) notation to describe the level of theory used in calculations.…”

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

“…224 The QM8 226,227 includes 20 000 synthetically available small molecules, and provides electronic structure values using both TD-DFT and CC2. The QM-symex 228 has 173 000 compounds with excited state energies calculated at the B3LYP/6-31G level, and provides particular emphasis on molecular symmetry. Such databases could be used for the calibration of lower-level methods, e.g.…”