Automated computational thermochemistry for butane oxidation: A prelude to predictive automated combustion kinetics

Keçeli, Murat; Elliott, Sarah N.; Li, Yi‐Pei; Johnson, Matthew S.; Cavallotti, Carlo; Georgievskii, Yuri; Green, William; Pelucchi, Matteo; Wozniak, Justin M.; Jasper, Ahren W.; Klippenstein, Stephen J.

doi:10.1016/j.proci.2018.07.113

Cited by 67 publications

(68 citation statements)

References 23 publications

(33 reference statements)

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“…Although recent advances in the field of ab initio quantum chemistry methods have facilitated quantitative understanding of challenging chemical problems [1][2][3][4][5] and accurate calculations of molecular thermochemistry, [6][7][8][9][10][11][12][13][14][15][16] large-scale theoretical studies are often still limited, at least initially, to the use of empirical methods to rapidly screen out unimportant species, so that only the important species are the subject of CPU-time intensive quantum chemistry calculations. Among the empirical methods developed in the past decades, the Benson group additivity scheme 17 is one of the quickest and most convenient methods to determine thermodynamic properties of molecules without requiring 3D molecular structures.…”

Section: Introductionmentioning

confidence: 99%

Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry

Han

Grambow

et al. 2019

J. Phys. Chem. A

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Because collecting precise and accurate chemistry data is often challenging, chemistry data sets usually only span a small region of chemical space, which limits the performance and the scope of applicability of data-driven models. To address this issue, we integrated an active learning machine with automatic ab initio calculations to form a selfevolving model that can continuously adapt to new species appointed by the users. In the present work, we demonstrate the self-evolving concept by modeling the formation enthalpies of stable closed-shell polycyclic species calculated at the B3LYP/6-31G(2df,p) level of theory. By combining a molecular graph convolutional neural network with a dropout training strategy, the model we developed can predict density functional theory (DFT) enthalpies for a broad range of polycyclic species and assess the quality of each predicted value. For the species which the current model is uncertain about, the automatic ab initio calculations provide additional training data to improve the performance of the model. For a test set composed of 2858 cyclic and polycyclic hydrocarbons and oxygenates, the enthalpies predicted by the model agree with the reference DFT values with a root-mean-square error of 2.62 kcal/mol. We found that a model originally trained on hydrocarbons and oxygenates can broaden its prediction coverage to nitrogen-containing species via an active learning process, suggesting that the continuous learning strategy is not only able to improve the model accuracy but is also capable of expanding the predictive capacity of a model to unseen species domains.

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Section: Introductionmentioning

confidence: 99%

Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry

Han

Grambow

et al. 2019

J. Phys. Chem. A

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“…In a similar spirit, we are developing analogous levels of support for VSVB calculations. Here, we describe Vtools , a Python code that depends on various modules: py3Dmol, EBSEL, Open Babel, obtools, and iotools . With the following minimum information given by the user, Vtools can currently generate Valence input files for any type of closed‐shell molecule in which the orbitals are doubly occupied: Chemical identifier: A SMILES or an InChI string, or a path to any file that can be recognized by Open Babel …”

Section: Methodsmentioning

confidence: 99%

Valence: A Massively Parallel Implementation of the Variational Subspace Valence Bond Method

et al. 2019

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This work describes the software package, Valence, for the calculation of molecular energies using the variational subspace valence bond (VSVB) method. VSVB is an ab initio electronic structure method based on nonorthogonal orbitals. Important features of practical value include high parallel scalability, wave functions that can be constructed automatically by combining orbitals from previous calculations, and ground and excited states that can be modeled with a single configuration or determinant. The open‐source software package includes tools to generate wave functions, a database of generic orbitals, example input files, and a library build intended for integration with other packages. We also describe the interface to an external software package, enabling the computation of optimized molecular geometries and vibrational frequencies. © 2019 Wiley Periodicals, Inc.

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“…On the contrary, if the model shows deviations outside of experimental uncertainties, relevant pathways that can be identified by means of analysis tools and model parameters are further refined with better estimates. Indeed, recent developments coupling high performance computing and theoretical chemistry allow the automatic generation of highly accurate parameters [11,29].…”

Section: Scenariomentioning

confidence: 99%

“…The efficient integration of the above tools and the process shown in Fig. 1 in a fully automatized system has been recognized as one of today's challenges in kinetic modelling [29]. Exponential growth in the volume and complexity of scientific information in the combustion community (experimental data, models, theoretical investigations, etc.)…”

Section: Scenariomentioning

confidence: 99%

Towards a scientific data framework to support scientific model development

Scalia

Pelucchi

Stagni

et al. 2019

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Editor: Silvio Peroni (https://orcid.org/0000-0003-0530-4305) Solicited reviews: HannaĆwiek-Kupczyńska (https://orcid.org/0000-0001-9113-567X); Aliaksandr Birukou (https://orcid.org/0000-0002-4925-9131); Stian Soiland-Reyes (https://orcid.org/0000-0001-9842-9718)Abstract. The sharing of scientific and scholarly data has been increasingly promoted over the last decade, leading to open repositories in many different scientific domains. However, data sharing and open data are not final goals in themselves, the real benefit is in data reuse, which allows leveraging investments in research and enables large-scale data-driven research progress.This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0). 2451-8484 © 2019 -IOS Press and the authors. G. Scalia et al. / Towards a scientific data framework to support scientific model developmentFocusing on reuse, this paper discusses the design of an integrated framework to automatically take advantage of large amounts of scientific data extracted from the literature to support research, and in particular scientific model development. Scientific models reproduce and predict complex phenomena and their development is a rather challenging task, within which scientific experiments have a key role in their continuous validation. Starting from the combustion kinetics domain, this paper discusses a set of use cases and a first prototype for such a framework which leads to a set of new requirements and an architecture that can be generalized to other domains. The paper analyzes the needs, the challenges and the research directions for such a framework, in particular those related to data management, automatic scientific model validation, data aggregation and data analysis, to leverage large amounts of published scientific data for new knowledge extraction.

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Automated computational thermochemistry for butane oxidation: A prelude to predictive automated combustion kinetics

Cited by 67 publications

References 23 publications

Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry

Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry

Valence: A Massively Parallel Implementation of the Variational Subspace Valence Bond Method

Towards a scientific data framework to support scientific model development

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