scholarly journals Prediction of Homolytic Bond Dissociation Enthalpies for Organic Molecules at near Chemical Accuracy with Sub-Second Computational Cost

2019 ◽  
Author(s):  
Peter St. John ◽  
Yanfei Guan ◽  
Yeonjoon Kim ◽  
Seonah Kim ◽  
Robert Paton
Keyword(s):  
Density Functional ◽  
Organic Molecules ◽  
Soot Formation ◽  
Chemical Reactivity ◽  
Hydrogen Abstraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Functional Theory ◽  
Small Organic Molecules ◽  
Bond Dissociation

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity. However, BDE computations at sufficiently high levels of quantum mechanical (QM) theory require substantial computing resources. We have therefore developed A machine-Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET), capable of accurately predicting BDEs for organic molecules in a fraction of a second. Automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory were performed for 42,577 small organic molecules, resulting in a dataset of 290,664 BDEs. A graph neural network was trained on a subset of these results, achieving a mean absolute error of 0.58 kcal/mol for the BDE values of unseen molecules. An interface for the developed prediction tool is available online at https://ml.nrel.gov/bde. The model rapidly and accurately predicts major sites of hydrogen abstraction in metabolism of drug-like molecules and determines the dominant molecular fragmentation pathways during soot formation.

scholarly journals Prediction of Homolytic Bond Dissociation Enthalpies for Organic Molecules at near Chemical Accuracy with Sub-Second Computational Cost

2019 ◽  
Author(s):  
Peter St. John ◽  
Yanfei Guan ◽  
Yeonjoon Kim ◽  
Seonah Kim ◽  
Robert Paton
Keyword(s):  
Density Functional ◽  
Organic Molecules ◽  
Soot Formation ◽  
Chemical Reactivity ◽  
Hydrogen Abstraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Functional Theory ◽  
Small Organic Molecules ◽  
Bond Dissociation

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity. However, BDE computations at sufficiently high levels of quantum mechanical (QM) theory require substantial computing resources. We have therefore developed A machine-Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET), capable of accurately predicting BDEs for organic molecules in a fraction of a second. Automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory were performed for 42,577 small organic molecules, resulting in a dataset of 290,664 BDEs. A graph neural network was trained on a subset of these results, achieving a mean absolute error of 0.58 kcal/mol for the BDE values of unseen molecules. An interface for the developed prediction tool is available online at https://ml.nrel.gov/bde. The model rapidly and accurately predicts major sites of hydrogen abstraction in metabolism of drug-like molecules and determines the dominant molecular fragmentation pathways during soot formation.

scholarly journals Prediction of Homolytic Bond Dissociation Enthalpies for Organic Molecules at near Chemical Accuracy with Sub-Second Computational Cost

2019 ◽  
Author(s):  
Peter St. John ◽  
Yanfei Guan ◽  
Yeonjoon Kim ◽  
Seonah Kim ◽  
Robert Paton
Keyword(s):  
Density Functional ◽  
Organic Molecules ◽  
Soot Formation ◽  
Chemical Reactivity ◽  
Hydrogen Abstraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Functional Theory ◽  
Small Organic Molecules ◽  
Bond Dissociation

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity. However, BDE computations at sufficiently high levels of quantum mechanical (QM) theory require substantial computing resources. We have therefore developed A machine-Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET), capable of accurately predicting BDEs for organic molecules in a fraction of a second. Automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory were performed for 42,577 small organic molecules, resulting in a dataset of 290,664 BDEs. A graph neural network was trained on a subset of these results, achieving a mean absolute error of 0.58 kcal/mol for the BDE values of unseen molecules. An interface for the developed prediction tool is available online at https://ml.nrel.gov/bde. The model rapidly and accurately predicts major sites of hydrogen abstraction in metabolism of drug-like molecules and determines the dominant molecular fragmentation pathways during soot formation.

scholarly journals Changes in CO2 Adsorption Affinity Related to Ni Doping in FeS Surfaces: A DFT-D3 Study

Catalysts ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 486
Author(s):  
Aleksandar Zivković ◽  
Michiel Somers ◽  
Eloi Camprubi ◽  
Helen E. King ◽  
Mariette Wolthers ◽  
...  
Keyword(s):  
Density Functional ◽  
Organic Molecules ◽  
Ni Doping ◽  
Functional Theory ◽  
Small Organic Molecules ◽  
Adsorption Affinity ◽  
The Origin Of Life ◽  
Carbon Dioxide Co2 ◽  
Partial Desorption ◽  
Good Agreement

Metal sulphides constitute cheap, naturally abundant, and environmentally friendly materials for energy storage applications and chemistry. In particular, iron (II) monosulphide (FeS, mackinawite) is a material of relevance in theories of the origin of life and for heterogenous catalytic applications in the conversion of carbon dioxide (CO2) towards small organic molecules. In natural mackinawite, Fe is often substituted by other metals, however, little is known about how such substitutions alter the chemical activity of the material. Herein, the effect of Ni doping on the structural, electronic, and catalytic properties of FeS surfaces is explored via dispersion-corrected density functional theory simulations. Substitutional Ni dopants, introduced on the Fe site, are readily incorporated into the pristine matrix of FeS, in good agreement with experimental measurements. The CO2 molecule was found to undergo deactivation and partial desorption from the doped surfaces, mainly at the Ni site when compared to undoped FeS surfaces. This behaviour is attributed to the energetically lowered d-band centre position of the doped surface, as a consequence of the increased number of paired electrons originating from the Ni dopant. The reaction and activation energies of CO2 dissociation atop the doped surfaces were found to be increased when compared to pristine surfaces, thus helping to further elucidate the role Ni could have played in the reactivity of FeS. It is expected that Ni doping in other Fe-sulphides may have a similar effect, limiting the catalytic activity of these phases when this dopant is present at their surfaces.

THEORETICAL STUDIES ON BOND DISSOCIATION ENERGIES FOR SOME THIOL COMPOUNDS BY DENSITRY FUNCTIONAL THEORY AND CBS-Q METHOD

2008 ◽  
Vol 07 (05) ◽  
pp. 943-951 ◽  
Author(s):  
XIAO-HONG LI ◽  
ZHENG-XIN TANG ◽  
ABRAHAM F. JALBOUT ◽  
XIAN-ZHOU ZHANG ◽  
XIN-LU CHENG
Keyword(s):  
Density Functional ◽  
Computational Cost ◽  
Basis Set ◽  
Basis Sets ◽  
Q Method ◽  
Bond Dissociation Energies ◽  
Functional Theory ◽  
Thiol Compounds ◽  
Bond Dissociation ◽  
Dissociation Energies

Quantum chemical calculations are used to estimate the bond dissociation energies (BDEs) for 15 thiol compounds. These compounds are studied by employing the hybrid density functional theory (B3LYP, B3PW91, B3P86, PBE0) methods and the complete basis set (CBS-Q) method together with 6-311G** basis set. It is demonstrated that B3P86 and CBS-Q methods are accurate for computing the reliable BDEs for thiol compounds. In order to test whether the non-local BLYP method suggested by Fu et al.19 is general for our study and whether B3P86 method has a low basis set sensitivity, the BDEs for seven thiol compounds are also calculated using BLYP/6-31+G* and B3P86 method with 6-31+G*, 6-31+G**, and 6-311+G** basis sets for comparison. The obtained results are compared with the available experimental results. It is noted that B3P86 method is not sensitive to the basis set. Considering the inevitable computational cost of CBS-Q method and the reliability of the B3P86 calculations, B3P86 method with a moderate or a larger basis set may be more suitable to calculate the BDEs of the C–SH bond for thiol compounds.

Green Synthesis of Triazole-Based Chemosensors and their Efficacy Towards Mercury Sensing

2020 ◽  
Vol 16 (6) ◽  
pp. 738-743 ◽  
Author(s):  
Poonam Rani ◽  
Kashmiri Lal ◽  
Vikas D. Ghule ◽  
Rahul Shrivastava
Keyword(s):  
Detection Limit ◽  
Metal Ions ◽  
Density Functional ◽  
Organic Molecules ◽  
Functional Theory ◽  
Small Organic Molecules ◽  
Industrial Activities ◽  
Binding Stoichiometry ◽  
Visible Spectra ◽  
Uv Visible

Background: The synthesis of small organic molecules based Hg2+ ions receptors have gained considerable attention because it is one of the most prevalent toxic metals which is continuously discharged into the environment by different natural and industrial activities. 1,4-Disubstituted 1,2,3-triazoles have been reported as good chemosensors for the detection of various metal ions including Hg2+ ions. Methods: The synthesis of 1,2,3-triazoles (4a-4c) was achieved by Cu(I)-catalyzed azide-alkyne cycloaddition, and their binding affinity towards various metal ions and anions were studied by UVVisible titration experiments. The perchlorate salts of metal ions and tetrabutylammonium salts of anions were utilized for the UV-Visible experiments. DFT studies were performed to understand the binding and mechanism on the sensing of 4a toward Hg2+ using the B3LYP/6-311G(d,p) method for 4a and B3LYP/LANL2DZ for 4a-Hg2+ species on the Gaussian 09W program. Results: The UV-visible experiments indicated that the compounds 4a-4c show a selective response towards Hg2+ ion in UV-Visible spectra, while other ions did not display such changes in the absorption spectra. The binding stoichiometry was evaluated by Job’s plot which indicated the 1:1 binding stoichiometry between receptors (4a-4c) and Hg2+ ion. The detection limit of 4a, 4b and 4c for the Hg2+ ions was found to be 29.1 nM, 3.5 μM and 1.34 μM, respectively. Conclusion: Some 1,2,3-triazole derivatives were synthesized (4a-4c) exhibiting high selectively and sensitivity towards Hg2+ ions in preference to other ions. Compound 4a has a low detection limit of 29.1 nM and the binding constant of 2.3×106 M-1. Similarly, 4b and 4c also showed selective sensing towards Hg2+ ions in the μM range. The observed experimental results were corroborated by density functional theory (DFT) calculations.

Assessment of PBE0 Calculation of C-NO2 Bond Dissociation Energies for Nitroaromatic System

2014 ◽  
Vol 915-916 ◽  
pp. 675-678
Author(s):  
Xin Fang Su ◽  
Wei Huang ◽  
Hai Ying Wu
Keyword(s):  
Density Functional ◽  
Molecular System ◽  
Absolute Error ◽  
Basis Sets ◽  
Bond Dissociation Energies ◽  
Average Absolute Error ◽  
Functional Theory ◽  
Bond Dissociation ◽  
Dissociation Energies ◽  
Experimental Values

Density functional theory (DFT) is used to calculate the C-NO2bond dissociation energies (BDEs) in nitrobenzene; 3-amino-nitrobenze; 4-amino-nitrobenze; 1,3-dinitrobenzene; 1,4-dinitrobenzene; 2-methyl-nitrobenzene; 4-methyl-nitrobenzene and 1,3,5-trinitrobenzene nitroaromatic molecular system. B3P86 and PBE0 methods in combination with 6-31G** and 6-311G** basis sets are employed. Comparison between the computational results and the experimental values reveals that the calculated C-NO2bond BDEs can be improved from B3P86 to PBE0 functional. Level of theory employing PBE0/6-311G** is found to be sufficiently reliable to compute BDEs of C-NO2bond for nitroaromatic molecules with an average absolute error of 0.98 kcal mol-1.

scholarly journals Machine learned features from density of states for accurate adsorption energy prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Victor Fung ◽  
Guoxiang Hu ◽  
P. Ganesh ◽  
Bobby G. Sumpter
Keyword(s):  
Density Of States ◽  
Density Functional ◽  
Heterogeneous Catalysts ◽  
Computational Cost ◽  
Absolute Error ◽  
Electronic Density ◽  
Functional Theory ◽  
Energy Prediction ◽  
Computational Screening ◽  
Machine Learning Model

AbstractMaterials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space.

Evaluating Transition Metal Barrier Heights with the Latest DFT Exchange–Correlation Functionals – the MOBH35 Benchmark Dataset

2019 ◽  
Author(s):  
Mark Iron ◽  
Trevor Janes
Keyword(s):  
Transition Metal ◽  
Density Functional ◽  
Computational Cost ◽  
Basis Set ◽  
Functional Theory ◽  
Exchange Correlation ◽  
Barrier Heights ◽  
Complete Basis Set ◽  
Complete Basis Set Limit ◽  
Hybrid Functionals

A new database of transition metal reaction barrier heights – MOBH35 – is presented. Benchmark energies (forward and reverse barriers and reaction energy) are calculated using DLPNO-CCSD(T) extrapolated to the complete basis set limit using a Weizmann1-like scheme. Using these benchmark energies, the performance of a wide selection of density functional theory (DFT) exchange–correlation functionals, including the latest from the Truhlar and Head-Gordon groups, is evaluated. It was found, using the def2-TZVPP basis set, that the ωB97M-V (MAD 1.8 kcal/mol), ωB97X-V (MAD 2.1 kcal/mol) and SCAN0 (MAD 2.1 kcal/mol) hybrid functionals are recommended. The double-hybrid functionals PWPB95 (MAD 1.6 kcal/mol) and B2K-PLYP (MAD 1.8 kcal/mol) did perform slightly better but this has to be balanced by their increased computational cost.

Benchmark of Simplified Time-Dependent Density Functional Theory for UV-Vis Spectral Properties of Porphyrinoids

2019 ◽  
Author(s):  
Kamal Batra ◽  
Stefan Zahn ◽  
Thomas Heine
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Large Scale ◽  
Absolute Error ◽  
Time Dependent ◽  
Basis Set ◽  
Basis Sets ◽  
Functional Theory ◽  
Framework Materials ◽  
Dependent Density

<p>We thoroughly benchmark time-dependent density- functional theory for the predictive calculation of UV/Vis spectra of porphyrin derivatives. With the aim to provide an approach that is computationally feasible for large-scale applications such as biological systems or molecular framework materials, albeit performing with high accuracy for the Q-bands, we compare the results given by various computational protocols, including basis sets, density-functionals (including gradient corrected local functionals, hybrids, double hybrids and range-separated functionals), and various variants of time-dependent density-functional theory, including the simplified Tamm-Dancoff approximation. An excellent choice for these calculations is the range-separated functional CAM-B3LYP in combination with the simplified Tamm-Dancoff approximation and a basis set of double-ζ quality def2-SVP (mean absolute error [MAE] of ~0.05 eV). This is not surpassed by more expensive approaches, not even by double hybrid functionals, and solely systematic excitation energy scaling slightly improves the results (MAE ~0.04 eV). </p>

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