26 research outputs found
π-Diradical Aromatic Soot Precursors in Flames.
No full textSoot emitted from incomplete combustion of hydrocarbon fuels contributes to global warming and causes human disease. The mechanism by which soot nanoparticles form within hydrocarbon flames is still an unsolved problem in combustion science. Mechanisms proposed to date involving purely chemical growth are limited by slow reaction rates, whereas mechanisms relying on solely physical interactions between molecules are limited by weak intermolecular interactions that are unstable at flame temperatures. Here, we show evidence for a reactive π-diradical aromatic soot precursor imaged using non-contact atomic force microscopy. Localization of π-electrons on non-hexagonal rings was found to allow for Kekulé aromatic soot precursors to possess a triplet diradical ground state. Barrierless chain reactions are shown between these reactive sites, which provide thermally stable aromatic rim-linked hydrocarbons under flame conditions. Quantum molecular dynamics simulations demonstrate physical condensation of aromatics that survive for tens of picoseconds. Bound internal rotors then enable the reactive sites to find each other and become chemically cross-linked before dissociation. These species provide a rapid, thermally stable chain reaction toward soot nanoparticle formation and could provide molecular targets for limiting the emission of these toxic combustion products
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A density functional theory study on the kinetics of seven-member ring formation in polyaromatic hydrocarbons
No full textIn this work, the kinetics of seven-member ring formation in polycyclic aromatic hydrocarbons (PAHs) containing a five-member ring is studied by density functional theory. The pathways studied include integration of a seven-member ring by the hydrogen-abstraction-acetylene-addition (HACA) mechanism for two different PAHs, one closed shell and one resonance-stabilised-radical (RSR) PAH. The pathways were similar in both cases, but the rate of seven-member ring formation by HACA was seen to be faster for the resonance-stabilised-radical PAH. Formation of a seven member ring by bay closure processes facilitated through hydrogen abstraction, hydrogen addition, carbene formation, and direct cyclisation were also studied for two PAHs. In general, the pathways were rather similar for both PAHs, aside from the direct cyclisation route. The rate constants determined for the pathways were then used in kinetic simulations in 0D homogeneous reactors. The results showed that for the RSR PAH, the initial abstraction site is important, with the seven-member ring mainly being formed when abstraction occurs near the five member ring. This was not the case for the closed shell PAH. Additionally, the RSR PAH product was able to undergo an azulene to naphthalene-like transformation at longer timescales. For the bay closures, it was seen for both PAHs that the hydrogen abstraction facilitated bay closures contributes the most to seven-member ring formation at temperatures up to 2000 K, but for very high temperatures of 2500 K, the carbene route becomes the most important contributor. The formation of seven-member rings occurred within 1 ms for all cases studied in the 0D reactors, suggesting that seven-member ring formation in PAHs containing a five-member ring is possible at flame temperatures
From the molecular quadrupole moment of oxygen to the macroscopic quadrupolarizability of its liquid phase
No full textAutomated Rational Design of Metal-Organic Polyhedra.
No full textMetal-organic polyhedra (MOPs) are hybrid organic-inorganic nanomolecules, whose rational design depends on harmonious consideration of chemical complementarity and spatial compatibility between two or more types of chemical building units (CBUs). In this work, we apply knowledge engineering technology to automate the derivation of MOP formulations based on existing knowledge. For this purpose we have (i) curated relevant MOP and CBU data; (ii) developed an assembly model concept that embeds rules in the MOP construction; (iii) developed an OntoMOPs ontology that defines MOPs and their key properties; (iv) input agents that populate The World Avatar (TWA) knowledge graph; and (v) input agents that, using information from TWA, derive a list of new constructible MOPs. Our result provides rapid and automated instantiation of MOPs in TWA and unveils the immediate chemical space of known MOPs, thus shedding light on new MOP targets for future investigations
When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?
No full textDeep graph neural networks are extensively utilized to predict chemical reactivity and molecular properties. However, because of the complexity of chemical space, such models often have difficulty extrapolating beyond the chemistry contained in the training set. Augmented model with quantum mechanical (QM) descriptors is anticipated to improve its generalizability. However, obtaining QM descriptors often requires CPU-intensive computational chemistry calculations. To identify when QM descriptors help graph neural networks predict chemical properties, we conduct a systematic investigation of the impact of atom, bond, and molecular QM descriptors on the performance of directed message passing neural networks (D-MPNNs) for predicting 16 molecular properties. The analysis surveys computational and experimental targets, classification and regression tasks, and varied dataset sizes from several hundred to hundreds of thousands of datapoints. Our results indicate that QM descriptors are mostly beneficial for D-MPNN performance on small datasets, provided that the descriptors correlate well with the targets and can be readily computed with high accuracy. Otherwise, using QM descriptors can add cost without benefit or even introduce unwanted noise that can degrade model performance. Strategic integration of QM descriptors with D-MPNN unlocks potential for physics-informed, data-efficient modeling with some interpretability that can streamline de novo drug and material designs. To facilitate the use of QM descriptors in machine learning workflows for chemistry, we provide a set of guidelines regarding when and how to best leverage QM descriptors, a high-throughput workflow to compute them, and an enhancement to Chemprop, a widely adopted open-source D-MPNN implementation for chemical property prediction
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Optical band gap of cross-linked, curved, and radical polyaromatic hydrocarbons.
No full textIn this work, the optical band gaps of polycyclic aromatic hydrocarbons (PAHs) crosslinked via an aliphatic bond, curved via pentagon integration and with radical character were computed using density functional theory. A variety of different functionals were benchmarked against optical band gaps (OBGs) measured by ultraviolet-visible spectroscopy with HSE06 being most accurate with a percentage error of 6% for a moderate basis set. Pericondensed aromatics with different symmetries were calculated with this improved functional providing new scaling relationships for the OBG versus size. Further calculations showed crosslinks cause a small decrease in the OBG of the monomers which saturates after 3-4 crosslinks. Curvature in PAHs was shown to increase the optical band gap due to the resulting change in hybridisation of the system, but this increase saturated at larger sizes. The increase in OBG between a flat PAH and a strained curved one was shown to be equivalent to a difference of several rings in size for pericondensed aromatic systems. The effect of σ-radicals on the optical band gap was also shown to be negligible, however, π-radicals were found to decrease the band gap by ∼0.5 eV. These findings have applications in understanding the molecular species involved in soot formation
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An Ontology and Semantic Web Service for Quantum Chemistry Calculations.
No full textThe purpose of this article is to present an ontology, termed OntoCompChem, for quantum chemistry calculations as performed by the Gaussian quantum chemistry software, as well as a semantic web service named MolHub. The OntoCompChem ontology has been developed based on the semantics of concepts specified in the CompChem convention of Chemical Markup Language (CML) and by extending the Gainesville Core (GNVC) ontology. MolHub is developed in order to establish semantic interoperability between different tools used in quantum chemistry and thermochemistry calculations, and as such is integrated into the J-Park Simulator (JPS)-a multidomain interactive simulation platform and expert system. It uses the OntoCompChem ontology and implements a formal language based on propositional logic as a part of its query engine, which verifies satisfiability through reasoning. This paper also presents a NASA polynomial use-case scenario to demonstrate semantic interoperability between Gaussian and a tool for thermodynamic data calculations within MolHub
Question answering system for chemistry—a semantic agent extension
No full textThis paper introduces an extension of a previously developed question answering (QA) system for chemistry, operating on a knowledge graph (KG) called Marie. This extension enables the automatic invocation of semantic agents to answer questions when static data is absent from the KG. The agents are semantically described using the agent ontology, OntoAgent, to enable automated agent discovery and invocation. The natural language processing (NLP) models of the QA system need to be trained in order to interpret questions to be answered by new agents. For this purpose, we extend OntoAgent so that it becomes possible to automatically create training material for the NLP models. We evaluate the extended QA system with two example chemistry-related agents and an evaluation question set. The evaluation result shows that the extension allows the QA system to discover the suitable agent and to invoke the agent by automatically constructing requests from the semantic agent description, thereby increasing the range of questions the QA system can answer.National Research Foundation (NRF)Published versionThis project is funded by the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. Part of this work was supported by Towards Turing 2.0 under the EPSRC Grant EP/W037211/1 and The Alan Turing Institute. M.K. gratefully acknowledges the support of the Alexander von Humboldt Foundation. X. Zhou acknowledges financial support provided CMCL Innovations
