Toward Accurate Quantum Mechanical Thermochemistry: (1) Extensible Implementation and Comparison of Bond Additivity Corrections and Isodesmic Reactions.

Obtaining accurate enthalpies of formation of chemical species, ΔHf, often requires empirical corrections that connect the results of quantum mechanical (QM) calculations with the experimental enthalpies of elements in their standard state. One approach is to use atomization energy corrections followed by bond additivity corrections (BACs), such as those defined by Petersson et al. or Anantharaman and Melius. Another approach is to utilize isodesmic reactions (IDRs) as shown by Buerger et al. We implement both approaches in Arkane, an open-source software that can calculate species thermochemistry using results from various QM software packages. In this work, we collect 421 reference species from the literature to derive ΔHf corrections and fit atomization energy corrections and BACs for 15 commonly used model chemistries. We find that both types of BACs yield similar accuracy, although Anantharaman- and Melius-type BACs appear to generalize better. Furthermore, BACs tend to achieve better accuracy than IDRs for commonly used model chemistries, and IDRs can be less robust because of the sensitivity to the chosen reference species and reactions. Overall, Anantharaman- and Melius-type BACs are our recommended approach for achieving accurate QM corrections for enthalpies.

Automated Rational Design of Metal-Organic Polyhedra.

Metal-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.

π-Diradical Aromatic Soot Precursors in Flames.

Soot 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.

Multiscale Cross-Domain Thermochemical Knowledge-Graph.

In this paper, we develop a set of software agents which improve a knowledge-graph containing thermodynamic data of chemical species by means of quantum chemical calculations and error-canceling balanced reactions. The knowledge-graph represents species-associated information by making use of the principles of linked data, as employed in the Semantic Web, where concepts correspond to vertices and relationships between the concepts correspond to edges of the graph. We implement this representation by means of ontologies, which formalize the definition of concepts and their relationships, as a critical step to achieve interoperability between heterogeneous data formats and software. The agents, which conduct quantum chemical calculations and derive the estimates of standard enthalpies of formation, update the knowledge-graph with newly obtained results, improving data values, and adding nodes and connections between them. A key distinguishing feature of our approach is that it extends an existing, general-purpose knowledge-graph, called J-Park Simulator (http://theworldavatar.com), and its ecosystem of autonomous agents, thus enabling seamless cross-domain applications in wider contexts. To this end, we demonstrate how quantum calculations can directly affect the atmospheric dispersion of pollutants in an industrial emission use-case.

Reactivity of Polycyclic Aromatic Hydrocarbon Soot Precursors: Kinetics and Equilibria.

The thermodynamics and kinetics of cross-linking reactions between PAHs of various reactive edge types that are observed in soot precursors are explored using density functional theory. The forward rate constants confirm that reactions involving aryl σ-radicals are faster than others, but rate constants for reactions between aryl σ-radicals and localized π-radicals can be as large or even larger than for two aryl σ-radicals. However, rates for all cross-linking reactions between small PAHs are likely too slow to explain soot formation. The equilibrium constants show that reactions involving σ and π-radical PAHs are the most favorable at flame temperatures. Equilibrium constants for larger PAHs show that the ability to form bonded-and-stacked structures results in enhanced equilibrium constants for the reaction of two large localized π-radicals compared to those for other edge types. This suggests that combined physical and chemical interactions between larger π-radical PAHs could be important in flame environments.

An Ontology and Semantic Web Service for Quantum Chemistry Calculations.

The 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.

Optical band gap of cross-linked, curved, and radical polyaromatic hydrocarbons.

In 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.

OntoPESScan: An Ontology for Potential Energy Surface Scans.

In this work, a new OntoPESScan ontology is developed for the semantic representation of one-dimensional potential energy surface (PES) scans, a central concept in computational chemistry. This ontology is developed in line with knowledge graph principles and The World Avatar (TWA) project. OntoPESScan is linked to other ontologies for chemistry in TWA, including OntoSpecies, which helps uniquely identify species along the PES and access their properties, and OntoCompChem, which allows the association of potential energy surfaces with quantum chemical calculations and the concepts used to derive them. A force-field fitting agent is also developed that makes use of the information in the OntoPESScan ontology to fit force fields to reactive surfaces of interest on the fly by making use of the empirical valence bond methodology. This agent is demonstrated to successfully parametrize two cases, namely, a PES scan on ethanol and a PES scan on a localized π-radical PAH hypothesized to play a role in soot formation during combustion. OntoPESScan is an extension to the capabilities of TWA and, in conjunction with potential further ontological support for molecular dynamics and reactions, will further progress toward an open, continuous, and self-growing knowledge graph for chemistry.

ConfSolv: Prediction of Solute Conformer-Free Energies across a Range of Solvents.

Predicting Gibbs free energy of solution is key to understanding the solvent effects on thermodynamics and reaction rates for kinetic modeling. Accurately computing solution free energies requires the enumeration and evaluation of relevant solute conformers in solution. However, even after generation of relevant conformers, determining their free energy of solution requires an expensive workflow consisting of several ab initio computational chemistry calculations. To help address this challenge, we generate a large data set of solution free energies for nearly 44,000 solutes with almost 9 million conformers calculated in 41 different solvents using density functional theory and COSMO-RS and quantify the impact of solute conformers on the solution free energy. We then train a message passing neural network to predict the relative solution free energies of a set of solute conformers, enabling the identification of a small subset of thermodynamically relevant conformers. The model offers substantial computational time savings with predictions usually substantially within 1 kcal/mol of the free energy of the solution calculated by using computational chemical methods.

Predicting Power Conversion Efficiency of Organic Photovoltaics: Models and Data Analysis.

In this paper, the ability of three selected machine learning neural and baseline models in predicting the power conversion efficiency (PCE) of organic photovoltaics (OPVs) using molecular structure information as an input is assessed. The bidirectional long short-term memory (gFSI/BiLSTM), attentive fingerprints (attentive FP), and simple graph neural networks (simple GNN) as well as baseline support vector regression (SVR), random forests (RF), and high-dimensional model representation (HDMR) methods are trained to both the large and computational Harvard clean energy project database (CEPDB) and the much smaller experimental Harvard organic photovoltaic 15 dataset (HOPV15). It was found that the neural-based models generally performed better on the computational dataset with the attentive FP model reaching a state-of-the-art performance with the test set mean squared error of 0.071. The experimental dataset proved much harder to fit, with all of the models exhibiting a rather poor performance. Contrary to the computational dataset, the baseline models were found to perform better than the neural models. To improve the ability of machine learning models to predict PCEs for OPVs, either better computational results that correlate well with experiments or more experimental data at well-controlled conditions are likely required.