OntoKin: An Ontology for Chemical Kinetic Reaction Mechanisms.

An ontology for capturing both data and the semantics of chemical kinetic reaction mechanisms has been developed. Such mechanisms can be applied to simulate and understand the behavior of chemical processes, for example, the emission of pollutants from internal combustion engines. An ontology development methodology was used to produce the semantic model of the mechanisms, and a tool was developed to automate the assertion process. As part of the development methodology, the ontology is formally represented using a web ontology language (OWL), assessed by domain experts, and validated by applying a reasoning tool. The resulting ontology, termed OntoKin, has been used to represent example mechanisms from the literature. OntoKin and its instantiations are integrated to create a knowledge base (KB), which is deployed using the RDF4J triple store. The use of the OntoKin ontology and the KB is demonstrated for three use cases-querying across mechanisms, modeling atmospheric pollution dispersion, and as a mechanism browser tool. As part of the query use case, the OntoKin tools have been applied by a chemist to identify variations in the rate of a prompt NOx formation reaction in the combustion of ammonia as represented by four mechanisms in the literature

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.This project is supported by the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, and by the Alexander von Humboldt foundation

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

Funder: Cambridge TrustFunder: National Research Foundation SingaporeFunder: Alexander von Humboldt-StiftungFunder: China Scholarship CouncilIn 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

Automated advanced calibration and optimization of thermochemical models applied to biomass gasification and pyrolysis

This paper presents a methodology that combines physicochemical modeling with advanced statistical analysis algorithms as an efficient workflow, which is then applied to the optimization and design of biomass pyrolysis and gasification processes. The goal was to develop an automated flexible approach for the analyses and optimization of such processes. The approach presented here can also be directly applied to other biomass conversion processes and, in general, to all those processes for which a parametrized model is available. A flexible physicochemical model of the process is initially formulated. Within this model, a hierarchy of sensitive model parameters and input variables (process conditions) is identified, which are then automatically adjusted to calibrate the model and to optimize the process. Through the numerical solution of the underlying mathematical model of the process, we can understand how species concentrations and the thermodynamic conditions within the reactor evolve for the two processes studied. The flexibility offered by the ability to control any model parameter is critical in enabling optimization of both efficiency of the process as well as its emissions. It allows users to design and operate feedstock-flexible pyrolysis and gasification processes, accurately control product characteristics, and minimize the formation of unwanted byproducts (e.g., tar in biomass gasification processes) by exploiting various productivity-enhancing simulation techniques, such as parameter estimation, computational surrogate (reduced order model) generation, uncertainty propagation, and multi-response optimization

Modelling Investigation of the Thermal Treatment of Ash-Contaminated Particulate Filters

Funder: National Research Foundation Singapore; doi: https://doi.org/10.13039/501100001381Funder: Shell United Kingdom; doi: https://doi.org/10.13039/100010893Abstract: This paper investigates the impact of thermal treatment on the pressure drop of particulate filters containing ash deposits. A one-dimensional model has been developed and applied to describe the deposition of soot and ash particles, and estimate the spatial distribution of the deposits in such filters. Phenomenological models have been developed to describe the potential sintering and cracking of the ash deposits caused by thermal treatment of the filter. The model results are in good agreement with experimental measurements of the reduction in the pressure drop in thermally treated filters. It was found that crack formation in the ash layer can lead to significant reduction of the pressure drop at relatively low temperatures. Sintering of ash deposits in the wall and the ash plug also contributes towards a decrease in filter pressure drop at higher temperatures. This work is the first attempt to model the impact of the thermal treatment of ash in particulate filters in order to support the development of future ash management strategies. The cracking of the ash layer during the thermal treatment has been identified to be the most critical effect for pressure drop reduction

Investigation of the impact of the configuration of exhaust after-treatment system for diesel engines

Exhaust After-Treatment (EAT) systems are necessary for automotive powertrains to meet stringent emission standards. Computational modelling has been applied to aid designing EAT systems. Models with global kinetic mechanisms are often used in practice, but they cannot accurately predict the behaviour of after-treatment devices under a wide range of conditions. In this study, a numerical EAT model with rigorous treatment of the catalytic chemistry is proposed to investigate the impact of the configuration of individual devices in the EAT system; one of the key design decisions. The performance of the proposed model is first critically assessed against experimental and simulation data from the literature before being applied to design a multi-device EAT system for a diesel engine. The target EAT system is composed of a diesel oxidation catalyst (DOC), an ammonia-based selective catalytic reduction (NH3-SCR) device and a diesel particulate filter (DPF). The steady state behaviour of various EAT designs under operating conditions across the engine map are examined. The DOC-DPF-SCR layout is found to be more beneficial than the alternative DOC-SCR-DPF for the specific engine studied. Furthermore, the DPF-front system is more robust with respect to changes in emission regulations. Flux analysis is applied to study the chemical interaction in the SCR and explain the disadvantage of the SCR-front system. In addition, it is demonstrated in the study that future catalyst investigations should consider more realistic feed compositions

Organic solar cell donor data for submitted paper: "Predicting power conversion efficiency of organic photovoltaics: models and data analysis"

This dataset is provided as supporting information for the submitted paper: "Predicting power conversion efficiency of organic photovoltaics: models and data analysis". The dataset consists of two CSV files. The first CSV file contains data on the characteristics of organic solar cell donor candidates obtained from the Harvard Clean Energy Project Database (CEPDB). This includes SMILES strings, HOMO, LUMO, and Scharber Equation parameters derived by means of density functional theory. The second CSV file contains experimental data obtained from the Harvard Organic Photovoltaic 15 (HOPV15) dataset, including SMILES strings, solar cell architecture, HOMO, LUMO, optical and electronic gaps, power conversion efficiency, open circuit voltages, short circuit currents, and fill factors

Question Answering System for Chemistry.

This paper describes the implementation and evaluation of a proof-of-concept Question Answering (QA) system for accessing chemical data from knowledge graphs (KGs) which offer data from chemical kinetics to the chemical and physical properties of species. We trained the question classification and named the entity recognition models that specialize in interpreting chemistry questions. The system has a novel design which applies a topic model to identify the question-to-ontology affiliation to handle ontologies with different structures. The topic model also helps the system to provide answers with a higher quality. Moreover, a new method that automatically generates training questions from ontologies is also implemented. The question set generated for training contains 432,989 questions under 11 types. Such a training set has been proven to be effective for training both the question classification model and the named entity recognition model. We evaluated the system using other KGQA systems as baselines. The system outperforms the chosen KGQA system answering chemistry-related questions. The QA system is also compared to the Google search engine and the WolframAlpha engine. It shows that the QA system can answer certain types of questions better than the search engines

Research Data Supporting "Theoretical study of the TiCl4=TiCl3+Cl reaction"

The data come from the simulations performed in the paper. They are divided into four folders: 1) Folder: Quantum-Data A) Subfolder: 2Dplot - Contains energies obtained in Molpro2012 for different positions of the TiCl3 and Cl fragments. This was used to create the 2D contour plot. B) Subfolder: Geoms-Freqs-Energies - Contains input/output files from the Gaussian09 software giving all the information about the species geometries, frequencies and energies. - Contains input/output files from the Molpro2012 software giving all the information about the species improved energies. C) Subfolder: SOC - Contains input/output files from the Molpro2012 software giving spin-orbit-coupling energies for the selected points along the minimum energy pathway. 2) Folder: Gorin-Model - Contains input/output files needed to reproduce all the Gorin Model simulations 3) Folder: VRC-TST - Contains input/output files needed to reproduce VRC-TST calculations 4) Folder: ME - Contains input/output files needed to reproduce Master Equation calculations including the sensitivity analysis as stated in the paper. Readme.txt - Contains detailed description of all the data.Huntsman Pigments and Additives (Grant ID: RG61437)EPSRC (Grant ID: EP/J500380/1)National Research Foundation (NRF)Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programmeU.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences under Contract No. DE-AC04-94AL85000

Theoretical Study of the Ti–Cl Bond Cleavage Reaction in TiCl4

In this work the kinetics of the TiCl4 ⇌ TiCl3 + Cl reaction is studied theoretically. A variable-reaction coordinate transition-state theory (VRC-TST) is used to calculate the high-pressure limit rate coefficients. The interaction energy surface for the VRC-TST step is sampled directly at the caspt(6e,4o)(cc-pVDZ)-1 level of theory including an approximate treatment of the spin-orbit coupling. The pressure-dependence of the reaction in an argon bath gas is explored using the master equation in conjunction with the optimised VRC-TST transition-state number of states. The collisional energy transfer parameters for the TiCl4–Ar system are estimated via a “one-dimensional minimisation” method and classical trajectories. The Ti–Cl bond dissociation energy is computed using a complete basis set extrapolation technique with cc-pVQZ and cc-pV5Z basis sets. Good quantitative agreement between the estimated rate constants and available literature data is observed. However, the fall-off behaviour of the model results is not seen in the current experimental data. Sensitivity analysis shows that the fall-off effect is insensitive to the choice of model parameters and methods. More experimental work and development of higher-level theoretical methods are needed to further investigate this discrepancy.NRF (Natl Research Foundation, S’pore)Published versio