scholarly journals Molecular Growth upon Ionization of Van Der Waals Clusters Containing HCCH and HCN is a Pathway to Prebiotic Molecules

2020 ◽  
Author(s):  
Tamar Stein ◽  
Partha P. Bera ◽  
Timothy J. Lee ◽  
Martin Head-Gordon
Keyword(s):  
Interstellar Medium ◽  
Density Functional ◽  
Organic Molecules ◽  
Chemical Reactivity ◽  
Van Der Waals ◽  
Ab Initio Molecular Dynamics ◽  
Covalent Bonds ◽  
Van Der Waals Clusters ◽  
Prebiotic Molecule ◽  
Mixed Clusters

The growth mechanisms of organic molecules in an ionizing environment such as the interstellar medium are not completely understood. Here we examine by means of ab initio molecular dynamics (AIMD) simulations and density functional theory (DFT) computations the possibility of bond formation and molecular growth upon ionization of Van der Waals clusters of pure HCN clusters, and mixed clusters of HCN and HCCH, both of which are widespread in the interstellar medium. Ionization of van der Waals clusters can potentially lead to growth in low temperature and low-density environments. Our results show, that upon ionization of the pure HCN clusters, strongly bound stable structures are formed that contain NH bonds, and growth beyond pairwise HCN molecules is seen only in a small percentage of cases. In contrast, mixed clusters, where HCCH is preferentially ionized over HCN, can grow up to 3 or 4 units long with new carbon-carbon and carbon-nitrogen covalent bonds. Moreover, cyclic molecules formed, such as the radical cation of pyridine, which is a prebiotic molecule. The results presented here are significant as they provide a feasible pathway for molecular growth of small organic molecules containing both carbon and nitrogen in cold and relatively denser environments such as in dense molecular clouds but closer to the photo-dissociation regions, and protoplanetary disks. In the mechanism we propose, first, a neutral van der Waals cluster is formed. Once the cluster is formed it can undergo photoionization which leads to chemical reactivity without any reaction barrier.

scholarly journals Molecular Growth upon Ionization of Van Der Waals Clusters Containing HCCH and HCN is a Pathway to Prebiotic Molecules

2020 ◽  
Author(s):  
Tamar Stein ◽  
Partha P. Bera ◽  
Timothy J. Lee ◽  
Martin Head-Gordon
Keyword(s):  
Interstellar Medium ◽  
Density Functional ◽  
Organic Molecules ◽  
Chemical Reactivity ◽  
Van Der Waals ◽  
Ab Initio Molecular Dynamics ◽  
Covalent Bonds ◽  
Van Der Waals Clusters ◽  
Prebiotic Molecule ◽  
Mixed Clusters

The growth mechanisms of organic molecules in an ionizing environment such as the interstellar medium are not completely understood. Here we examine by means of ab initio molecular dynamics (AIMD) simulations and density functional theory (DFT) computations the possibility of bond formation and molecular growth upon ionization of Van der Waals clusters of pure HCN clusters, and mixed clusters of HCN and HCCH, both of which are widespread in the interstellar medium. Ionization of van der Waals clusters can potentially lead to growth in low temperature and low-density environments. Our results show, that upon ionization of the pure HCN clusters, strongly bound stable structures are formed that contain NH bonds, and growth beyond pairwise HCN molecules is seen only in a small percentage of cases. In contrast, mixed clusters, where HCCH is preferentially ionized over HCN, can grow up to 3 or 4 units long with new carbon-carbon and carbon-nitrogen covalent bonds. Moreover, cyclic molecules formed, such as the radical cation of pyridine, which is a prebiotic molecule. The results presented here are significant as they provide a feasible pathway for molecular growth of small organic molecules containing both carbon and nitrogen in cold and relatively denser environments such as in dense molecular clouds but closer to the photo-dissociation regions, and protoplanetary disks. In the mechanism we propose, first, a neutral van der Waals cluster is formed. Once the cluster is formed it can undergo photoionization which leads to chemical reactivity without any reaction barrier.

scholarly journals Molecular dynamics reveals formation path of benzonitrile and other molecules in conditions relevant to the interstellar medium

2021 ◽  
Vol 118 (19) ◽  
pp. e2101371118
Author(s):  
Jeeno Jose ◽  
Alon Zamir ◽  
Tamar Stein
Keyword(s):  
Molecular Dynamics ◽  
Interstellar Medium ◽  
Bond Formation ◽  
Ab Initio Molecular Dynamics ◽  
Complex Molecules ◽  
Van Der Waals Clusters ◽  
Aromatic Molecules ◽  
Polycyclic Aromatic ◽  
The Rich ◽  
Mixed Clusters

Polycyclic aromatic hydrocarbons and polycyclic aromatic nitrogen heterocycles are believed to be widespread in different areas of the interstellar medium. However, the astronomical detection of specific aromatic molecules is extremely challenging. As a result, only a few aromatic molecules have been identified, and very little is known about how they are formed in different areas of the interstellar medium. Recently, McGuire et al. [Science 359, 202–205 (2018)] detected the simple aromatic molecule benzonitrile in Taurus Molecular Cloud-1. Although benzonitrile has been observed, the molecular mechanism for its formation is still unknown. In this study, we use quantum chemistry and ab initio molecular dynamics to model ionization processes of van der Waals clusters containing cyanoacetylene and acetylene molecules. We demonstrate computationally that the clusters' ionization leads to molecular formation. For pure cyanoacetylene clusters, we observe bond formation among two and three monomer units, whereas in mixed clusters, bond formation can also occur in up to four units. We show that the large amount of energy available to the system after ionization can lead to barrier crossing and the formation of complex molecules. Our study reveals the rich chemistry that is observed upon ionization of the clusters, with a wide variety of molecules being formed. Benzonitrile is among the observed molecules, and we study the potential energy path for its formation. These results also offer insights that can guide astronomers in their search for aromatic molecules in the interstellar medium.

scholarly journals Teaching a Neural Network to Attach and Detach Electrons from Molecules

2021 ◽  
Author(s):  
Roman Zubatyuk ◽  
Justin Smith ◽  
Benjamin T. Nebgen ◽  
Sergei Tretiak ◽  
Olexandr Isayev
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Organic Molecules ◽  
Chemical Elements ◽  
Chemical Reactivity ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Electrophilic Aromatic Substitution ◽  
Interatomic Potentials ◽  
Conceptual Density Functional Theory

<p></p><p>Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry. In particular, interatomic potentials derived with Machine Learning algorithms such as Deep Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. The applicability domain of DNN potentials is usually limited by the type of training data. As such, transferable models are aimed to be extensible in the description of chemical and conformational diversity of organic molecules. However, most DNN potentials, such as the AIMNet model we proposed previously, were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we extend machine learning framework toward open-shell anions and cations. We introduce AIMNet-NSE (Neural Spin Equilibration) architecture, which being properly trained, could predict atomic and molecular properties for an arbitrary combination of molecular charge and spin multiplicity. This model explores a new dimension of transferability by adding the charge-spin space. The AIMNet-NSE model is capable of reproducing reference QM energies for cations, neutrals, and anions with errors of about 2-3 kcal/mol, compared to the reference QM simulations. The spin-charges have errors ~0.01 electrons for small organic molecules containing nine chemical elements {H, C, N, O, F, Si, P, S and Cl}. <a>The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions with a speed up to 10<sup>4</sup> molecules per second on a single modern GPU.</a> We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions.</p><p></p>

scholarly journals Teaching a Neural Network to Attach and Detach Electrons from Molecules

2020 ◽  
Author(s):  
Roman Zubatyuk ◽  
Justin Smith ◽  
Benjamin T. Nebgen ◽  
Sergei Tretiak ◽  
Olexandr Isayev
Keyword(s):  
Density Functional ◽  
Organic Molecules ◽  
Chemical Elements ◽  
Chemical Reactivity ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Electrophilic Aromatic Substitution ◽  
Interatomic Potentials ◽  
Conceptual Density Functional Theory ◽  
Fukui Functions

<p>Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry. In particular, interatomic potentials derived with Machine Learning algorithms such as Deep Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. The applicability domain of DNN potentials is usually limited by the type of training data. As such, transferable models are aimed to be extensible in the description of chemical and conformational diversity of organic molecules. However, most DNN potentials, such as the AIMNet model we proposed previously, were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we extend our AIMNet framework toward open-shell anions and cations. This model explores a new dimension of transferability by adding the charge-spin space. The resulting AIMNet model is capable of reproducing reference QM energies for cations, neutrals and anions with errors of 4.1, 2.1, 2.8 kcal/mol, respectively, compared to the reference QM simulations. The spin-charges have errors 0.01-0.06 electrons for small organic molecules containing nine chemical elements {H, C, N, O, F, Si, P, S and Cl}. Thus the proposed AIMNet model allows researchers to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regionselectivity in electrophilic aromatic substitution reactions.</p>

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 Teaching a Neural Network to Attach and Detach Electrons from Molecules

2020 ◽  
Author(s):  
Roman Zubatyuk ◽  
Justin Smith ◽  
Benjamin T. Nebgen ◽  
Sergei Tretiak ◽  
Olexandr Isayev
Keyword(s):  
Density Functional ◽  
Organic Molecules ◽  
Chemical Elements ◽  
Chemical Reactivity ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Electrophilic Aromatic Substitution ◽  
Interatomic Potentials ◽  
Conceptual Density Functional Theory ◽  
Fukui Functions

<p>Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry. In particular, interatomic potentials derived with Machine Learning algorithms such as Deep Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. The applicability domain of DNN potentials is usually limited by the type of training data. As such, transferable models are aimed to be extensible in the description of chemical and conformational diversity of organic molecules. However, most DNN potentials, such as the AIMNet model we proposed previously, were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we extend our AIMNet framework toward open-shell anions and cations. This model explores a new dimension of transferability by adding the charge-spin space. The resulting AIMNet model is capable of reproducing reference QM energies for cations, neutrals and anions with errors of 4.1, 2.1, 2.8 kcal/mol, respectively, compared to the reference QM simulations. The spin-charges have errors 0.01-0.06 electrons for small organic molecules containing nine chemical elements {H, C, N, O, F, Si, P, S and Cl}. Thus the proposed AIMNet model allows researchers to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regionselectivity in electrophilic aromatic substitution reactions.</p>

Adsorption of alcohols and hydrocarbons on nonstoichiometric cementite{010} surfaces

2018 ◽  
Vol 20 (20) ◽  
pp. 14133-14144 ◽  
Author(s):  
David Muñoz Ramo ◽  
Stephen J. Jenkins
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Organic Molecules ◽  
Van Der Waals ◽  
Density Functional Theory Calculations ◽  
Functional Theory

We investigate the adsorption of several organic molecules on a nonstoichiometric {010} surface of Fe3C (cementite) by means of density functional theory calculations with van der Waals corrections.

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.

Density functional calculations of Van der Waals clusters: NeN2+ as a case study

1997 ◽  
Vol 276 (1-2) ◽  
pp. 9-12
Author(s):  
V. Subramanian ◽  
K. Venkatesh ◽  
D.Mary Prabha ◽  
T. Ramasami
Keyword(s):  
Density Functional Calculations ◽  
Density Functional ◽  
Van Der Waals ◽  
Van Der Waals Clusters
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