scholarly journals Computational Discovery of TTF Molecules with Deep Generative Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Alexander Yakubovich ◽  
Alexey Odinokov ◽  
Sergey Nikolenko ◽  
Yongsik Jung ◽  
Hyeonho Choi
Keyword(s):  
Quantum Chemical Calculations ◽  
Quantum Chemical ◽  
Computer Aided Design ◽  
Chemical Space ◽  
Energy Levels ◽  
Generative Models ◽  
Excitation Energies ◽  
Efficient Energy Transfer ◽  
Computational Evaluation ◽  
Computational Discovery

We present a computational workflow based on quantum chemical calculations and generative models based on deep neural networks for the discovery of novel materials. We apply the developed workflow to search for molecules suitable for the fusion of triplet-triplet excitations (triplet-triplet fusion, TTF) in blue OLED devices. By applying generative machine learning models, we have been able to pinpoint the most promising regions of the chemical space for further exploration. Another neural network based on graph convolutions was trained to predict excitation energies; with this network, we estimate the alignment of energy levels and filter molecules before running time-consuming quantum chemical calculations. We present a comprehensive computational evaluation of several generative models, choosing a modification of the Junction Tree VAE (JT-VAE) as the best one in this application. The proposed approach can be useful for computer-aided design of materials with energy level alignment favorable for efficient energy transfer, triplet harvesting, and exciton fusion processes, which are crucial for the development of the next generation OLED materials.

Energy Levels and Electromagnetic Transition of 90-94mo Nuclei Using Ibm-1

2020 ◽  
pp. 149-152
Keyword(s):  
Experimental Data ◽  
Transition Probability ◽  
Energy Levels ◽  
Dynamical Symmetry ◽  
Interacting Boson Model ◽  
Excitation Energies ◽  
Electromagnetic Transition ◽  
Boson Model ◽  
Energy States ◽  
Good Agreement

The energy states for the J , b , ɤ bands and electromagnetic transitions B (E2) values for even – even molybdenum 90 – 94 Mo nuclei are calculated in the present work of "the interacting boson model (IBM-1)" . The parameters of the equation of IBM-1 Hamiltonian are determined which yield the best excellent suit the experimental energy states . The positive parity of energy states are obtained by using IBS1. for program for even 90 – 94 Mo isotopes with bosons number 5 , 4 and 5 respectively. The" reduced transition probability B(E2)" of these neuclei are calculated and compared with the experimental data . The ratio of the excitation energies of the 41+ to 21+ states ( R4/2) are also calculated . The calculated and experimental (R4/2) values showed that the 90 – 94 Mo nuclei have the vibrational dynamical symmetry U(5). Good agreement was found from comparison between the calculated energy states and electric quadruple probabilities B(E2) transition of the 90–94Mo isotopes with the experimental data .

Discovery of a Difluoroglycine Synthesis Method through Quantum Chemical Calculations

2020 ◽  
Author(s):  
Tsuyoshi Mita ◽  
Yu Harabuchi ◽  
Satoshi Maeda
Keyword(s):  
Quantum Chemical Calculations ◽  
Experimental Verification ◽  
Quantum Chemical ◽  
Synthesis Method ◽  
Target Product ◽  
Systematic Exploration ◽  
Hands On ◽  
Retrosynthetic Analysis ◽  
Synthetic Routes ◽  
Verification Process

The systematic exploration of synthetic pathways to afford a desired product through quantum chemical calculations remains a considerable challenge. In 2013, Maeda et al. introduced ‘quantum chemistry aided retrosynthetic analysis’ (QCaRA), which uses quantum chemical calculations to search systematically for decomposition paths of the target product and propose a synthesis method. However, until now, no new reactions suggested by QCaRA have been reported to lead to experimental discoveries. Using a difluoroglycine derivative as a target, this study investigated the ability of QCaRA to suggest various synthetic paths to the target without relying on previous data or the knowledge and experience of chemists. Furthermore, experimental verification of the seemingly most promising path led to the discovery of a synthesis method for the difluoroglycine derivative. The extent of the hands-on expertise of chemists required during the verification process was also evaluated. These insights are expected to advance the applicability of QCaRA to the discovery of viable experimental synthetic routes.

Discovery of a Difluoroglycine Synthesis Method through Quantum Chemical Calculations

2020 ◽  
Author(s):  
Tsuyoshi Mita ◽  
Yu Harabuchi ◽  
Satoshi Maeda
Keyword(s):  
Quantum Chemical Calculations ◽  
Experimental Verification ◽  
Quantum Chemical ◽  
Synthesis Method ◽  
Target Product ◽  
Systematic Exploration ◽  
Hands On ◽  
Retrosynthetic Analysis ◽  
Synthetic Routes ◽  
Verification Process

The systematic exploration of synthetic pathways to afford a desired product through quantum chemical calculations remains a considerable challenge. In 2013, Maeda et al. introduced ‘quantum chemistry aided retrosynthetic analysis’ (QCaRA), which uses quantum chemical calculations to search systematically for decomposition paths of the target product and propose a synthesis method. However, until now, no new reactions suggested by QCaRA have been reported to lead to experimental discoveries. Using a difluoroglycine derivative as a target, this study investigated the ability of QCaRA to suggest various synthetic paths to the target without relying on previous data or the knowledge and experience of chemists. Furthermore, experimental verification of the seemingly most promising path led to the discovery of a synthesis method for the difluoroglycine derivative. The extent of the hands-on expertise of chemists required during the verification process was also evaluated. These insights are expected to advance the applicability of QCaRA to the discovery of viable experimental synthetic routes.

The Nature of Chemisorbed CO2 in Zeolite A

2019 ◽  
Author(s):  
Przemyslaw Rzepka ◽  
Zoltán Bacsik ◽  
Andrew J. Pell ◽  
Niklas Hedin ◽  
Aleksander Jaworski
Keyword(s):  
Solid State ◽  
Ir Spectroscopy ◽  
Quantum Chemical Calculations ◽  
Quantum Chemical ◽  
Surface Coverage ◽  
Gas Mixtures ◽  
Mas Nmr ◽  
Significant Fraction ◽  
Zeolite A

Formation of CO<sub>3</sub><sup>2-</sup> and HCO<sub>3</sub><sup>-</sup> species without participation of the framework oxygen atoms upon chemisorption of CO<sub>2</sub> in zeolite |Na<sub>12</sub>|-A is revealed. The transfer of O and H atoms is very likely to have proceeded via the involvement of residual H<sub>2</sub>O or acid groups. A combined study by solid-state <sup>13</sup>C MAS NMR, quantum chemical calculations, and <i>in situ</i> IR spectroscopy showed that the chemisorption mainly occurred by the formation of HCO<sub>3</sub><sup>-</sup>. However, at a low surface coverage of physisorbed and acidic CO<sub>2</sub>, a significant fraction of the HCO<sub>3</sub><sup>-</sup> was deprotonated and transformed into CO<sub>3</sub><sup>2-</sup>. We expect that similar chemisorption of CO<sub>2</sub> would occur for low-silica zeolites and other basic silicates of interest for the capture of CO<sub>2</sub> from gas mixtures.

Mechanistic Investigation of Rh(I)-Catalyzed Asymmetric Suzuki-Miyaura Coupling with Racemic Allyl Halides

2019 ◽  
Author(s):  
Lucy van Dijk ◽  
Ruchuta Ardkhean ◽  
Mireia Sidera ◽  
Sedef Karabiyikoglu ◽  
Özlem Sari ◽  
...  
Keyword(s):  
Quantum Chemical Calculations ◽  
Quantum Chemical ◽  
Experimental Studies ◽  
Mechanistic Investigation ◽  
Allyl Halides

A mechanism for Rh(I)-catalyzed asymmetric Suzuki-Miyaura coupling with racemic allyl halides is proposed based on a combination of experimental studies and quantum chemical calculations. <br>

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

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