Heavy-Element Reactions Database (HERDB): Relativistic ab Initio Geometries and Energies for Actinide Compounds

Andreadi, Nikolai; Mitrofanov, Artem; Matveev, Petr I.; Volkova, Anna; Kalmykov, Stepan N.

doi:10.1021/acs.inorgchem.0c01746

Cited by 7 publications

(3 citation statements)

References 80 publications

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“…This list includes substances related to the biological target activities or the substances for which synthesis and measurement are difficult or impossible for some reason. 14,15 One of the most promising methods of working with such data sets is the transfer learning approach. Recently, this methodology has found major interest in the scientific community, including chemistry and materials science fields.…”

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confidence: 99%

“…One of the main limitations for the application of ANNs in modern cheminformatics is a demand for large sets of data (hundreds to millions of data points) for model training. ,, At the same time, most of the chemical and biological properties do not have enough experimental data collected; the sizes of data sets for such properties often do not exceed tens or hundreds of compounds. This list includes substances related to the biological target activities or the substances for which synthesis and measurement are difficult or impossible for some reason. , …”

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confidence: 99%

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Size Doesn’t Matter: Predicting Physico- or Biochemical Properties Based on Dozens of Molecules

Karpov

Mitrofanov

Korolev

et al. 2021

J. Phys. Chem. Lett.

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The use of machine learning in chemistry has become a common practice. At the same time, despite the success of modern machine learning methods, the lack of data limits their use. Using a transfer learning methodology can help solve this problem. This methodology assumes that a model built on a sufficient amount of data captures general features of the chemical compound structure on which it was trained and that the further reuse of these features on a data set with a lack of data will greatly improve the quality of the new model. In this paper, we develop this approach for small organic molecules, implementing transfer learning with graph convolutional neural networks. The paper shows a significant improvement in the performance of the models for target properties with a lack of data. The effects of the data set composition on the model’s quality and the applicability domain of the resulting models are also considered.

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confidence: 99%

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confidence: 99%

Size Doesn’t Matter: Predicting Physico- or Biochemical Properties Based on Dozens of Molecules

Karpov

Mitrofanov

Korolev

et al. 2021

J. Phys. Chem. Lett.

Self Cite

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“…75 Furthermore, an examination of spin multiplicities of actinoid compounds as reported by Andreadi et al aligned with this adopted methodology, prompting its extrapolation for lanthanoids. 76 In total, 2406 closed-shell systems are included in the data set, for higher spin multiplicities up to +8 over 2200 samples are available per multiplicity.…”

Section: Creation Of the Lnqm Data Setmentioning

confidence: 99%

Hybrid DFT Geometries and Properties for 17k Lanthanoid Complexes─The LnQM Data Set

Hölzer,

Gordiy,

Grimme

et al. 2024

J. Chem. Inf. Model.

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The unique properties of lanthanoids and their diverse applications make them an indispensable part of modern research and industry. While the field has garnered attention, there remains a gap in available molecule data sets that facilitate both classical quantum chemistry calculations and the burgeoning field of machine learning in data science applications. This research addresses the need for a comprehensive data set that allows for a comparative analysis of various lanthanoids. The herein presented, curated data set includes 17269 monolanthanoid complexes derived from 1205 distinct ligand motifs. Structures encompass all 15 lanthanoids in the +3 oxidation state and exhibit molecular charges ranging from −1 to +3, including structures with a high spin multiplicity up to 8. Starting from lanthanum complexes, samples were processed with a permutation of the central lanthanoid atom, resulting in highly comparable subsets, facilitating comparative studies in which the influence of the lanthanoid can be investigated independently of ligand effects. The data set provides a broad range of features such as PBE0-D4/def2-SVP optimized geometries and optimization trajectories, while also covering ωB97M−V/def2-SVPD energies, rotational constants, dipole moments, highest occupied molecular orbital−lowest-unoccupied molecular orbital (HOMO−LUMO) energies, and Mulliken, Loẅdin, and Hirshfeld population analyses. Additionally, coordination numbers, polarizabilities, and partial charges from D4, electronegativity equilibration (EEQ), GFN2-xTB, and charge extended Huckel (CEH) calculations are included. The data set is openly accessible and may serve as a basis for further investigations into the properties of lanthanoids.

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Quantum Chemistry of d- and f-Block Elements

Autschbach

2024

Comprehensive Computational Chemistry

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Heavy-Element Reactions Database (HERDB): Relativistic ab Initio Geometries and Energies for Actinide Compounds

Cited by 7 publications

References 80 publications

Size Doesn’t Matter: Predicting Physico- or Biochemical Properties Based on Dozens of Molecules

Size Doesn’t Matter: Predicting Physico- or Biochemical Properties Based on Dozens of Molecules

Hybrid DFT Geometries and Properties for 17k Lanthanoid Complexes─The LnQM Data Set

Quantum Chemistry of d- and f-Block Elements

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