Thomas C. Allison

National Institute of Standards and Technology

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Donald G. Truhlar

University of Minnesota

YuYe J. Tong

Georgetown University

Lisa A. Fredin

Lehigh University

David W. Schwenke

Ames Research Center

Emily G. Bittle

National Institute of Standards and Technology

Steven L. Mielke

University of Minnesota

Adam J. Biacchi

National Institute of Standards and Technology

Andrew A. Herzing

Material Measurement Laboratory

Angela R. Hight Walker

Physical Measurement Laboratory

Chen Qu

Atlanta University Center

Cooperative Institutions

National Institute of Standards and Technology

Argonne National Laboratory

National Institute of Standards

Material Measurement Laboratory

University of Minnesota

Pacific Northwest National Laboratory

University of Maryland, College Park

Office of Scientific and Technical Information

Georgetown University

Cornell University

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Predicting Kovats Retention Indices Using Graph Neural Networks

arXiv (Cornell University) (2020)

Chen Qu Barry I. Schneider Anthony J. Kearsley Walid Keyrouz Thomas C. Allison

The \kovats retention index is a dimensionless quantity that characterizes the rate at which a compound is processed through a gas chromatography column. This quantity is independent of many experimental variables and, as such, is considered a near-universal descriptor of retention time on a chromatography column. The \kovats retention indices of a large number of molecules have been determined experimentally. The "NIST 20: GC Method\slash Retention Index Library" database has collected and, more importantly, curated retention indices of a subset of these compounds resulting in a highly valued reference database. The experimental data in the library form an ideal data set for training machine learning models for the prediction of retention indices of unknown compounds. In this article, we describe the training of a graph neural network model to predict the \kovats retention index for compounds in the NIST library and compare this approach with previous work \cite{2019Matyushin}. We predict the \kovats retention index with a mean unsigned error of 28 index units as compared to 44, the putative best result using a convolutional neural network \cite{2019Matyushin}. The NIST library also incorporates an estimation scheme based on a group contribution approach that achieves a mean unsigned error of 114 compared to the experimental data. Our method uses the same input data source as the group contribution approach, making its application straightforward and convenient to apply to existing libraries. Our results convincingly demonstrate the predictive powers of systematic, data-driven approaches leveraging deep learning methodologies applied to chemical data and for the data in the NIST 20 library outperform previous models.

Kovats retention index

NIST

10.48550/arxiv.2012.14798

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Application of an Artificial Neural Network to the Prediction of OH Radical Reaction Rate Constants for Evaluating Global Warming Potential

The Journal of Physical Chemistry B (2015)

Thomas C. Allison

Rate constants for reactions of chemical compounds with hydroxyl radical are a key quantity used in evaluating the global warming potential of a substance. Experimental determination of these rate constants is essential, but it can also be difficult and time-consuming to produce. High-level quantum chemistry predictions of the rate constant can suffer from the same issues. Therefore, it is valuable to devise estimation schemes that can give reasonable results on a variety of chemical compounds. In this article, the construction and training of an artificial neural network (ANN) for the prediction of rate constants at 298 K for reactions of hydroxyl radical with a diverse set of molecules is described. Input to the ANN consists of counts of the chemical bonds and bends present in the target molecule. The ANN is trained using 792 •OH reaction rate constants taken from the NIST Chemical Kinetics Database. The mean unsigned percent error (MUPE) for the training set is 12%, and the MUPE of the testing set is 51%. It is shown that the present methodology yields rate constants of reasonable accuracy for a diverse set of inputs. The results are compared to high-quality literature values and to another estimation scheme. This ANN methodology is expected to be of use in a wide range of applications for which •OH reaction rate constants are required. The model uses only information that can be gathered from a 2D representation of the molecule, making the present approach particularly appealing, especially for screening applications.

10.1021/acs.jpcb.5b09558

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Weighted Visibility Classification and Seating Plan for the Aitken University Centre

The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences (2014)

Thomas C. Allison David Fraser Emmanuel Stefanakis

Abstract. This paper describes the requirements for a weighted visibility classification of seats in the University of New Brunswick’s (UNB) Aitken University Centre (AUC). Price levels for seats are typically set for sections of seats based on promoter preferences. In a visibility classification, the digital elevation model (DEM) is created for the AUC and includes possible view obstructions. The view obstructions taken into account for this design were the penalty boxes, player bencher, and the rink boards. There were no other major obstructions in the AUC. The visibility calculations compute the number of visible pixels of the rink surface for each seat. It is expected that seats with a higher number of visible pixels will also have better visibility. The number of viewable pixels is weighted by distance to the center of ice surface to account for the preference of seats that are closer to the rink surface. This paper outlines the collection of data, weighted visibility classification method, and the development of information products. There are two main objectives of this weighted visibility classification and seating plan: (a) to demonstrate that a weighted visibility classification is a viable method to classify seats, and that this methodology could be used to set price levels for a venue and (b) create online web applications to suit the functionality for users and venue administrators. The user web application allows the user to pan, zoom and perform limited searches in the interactive map.

Visibility

10.5194/isprsarchives-xl-2-227-2014

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TESTING THE ACCURACY OF PRACTICAL SEMICLASSICAL METHODS: VARIATIONAL TRANSITION STATE THEORY WITH OPTIMIZED MULTIDIMENSIONAL TUNNELING

WORLD SCIENTIFIC eBooks (1998)

Thomas C. Allison Donald G. Truhlar

Semiclassical physics

Transition state theory

10.1142/9789812812162_0016

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Thinking Globally, Siting Locally: Renewable Energy and Biodiversity in a 4C World

AGU Fall Meeting Abstracts (2012)

Thomas C. Allison Peter C. Frumhoff Terry L. Root

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Uncertainty Quantification for Materials Properties in Density Functional Theory with k-Point Density

arXiv (Cornell University) (2020)

Joshua J. Gabriel Faical Yannick C. Congo Alexander Sinnott Kiran Mathew Thomas C. Allison

Many computational databases emerged over the last five years that report material properties calculated with density functional theory. The properties in these databases are commonly calculated to a precision that is set by choice of the basis set and the k-point density for the Brillouin zone integration. We determine how the precision of properties obtained from the Birch equation of state for 29 transition metals and aluminum in the three common structures -- fcc, bcc, and hcp -- correlate with the k-point density and the precision of the energy. We show that the precision of the equilibrium volume, bulk modulus, and the pressure derivative of the bulk modulus correlate comparably well with the k-point density and the precision of the energy, following an approximate power law. We recommend the k-point density as the convergence parameter because it is computationally efficient, easy to use as a direct input parameter, and correlates with property precision at least as well as the energy precision. We predict that common k-point density choices in high throughput DFT databases result in precision for the volume of 0.1%, the bulk modulus of 1%, and the pressure derivative of 10%.

Brillouin zone

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DFT-Based Permutationally Invariant Polynomial Potentials Capture the Twists and Turns of C14H30

Joel M. Bowman Chen Qu Paul L. Houston Thomas C. Allison Barry I. Schneider

Hydrocarbons are ubiquitous as fuels, solvents, lubricants, and as the principal components of plastics and fi�bers, yet our ability to predict their dynamical properties is limited to force-�eld mechanics. Here, we report two machine-learned potential energy surfaces (PESs) for the linear 44-atom hydrocarbon C14H30, using an extensive data set of roughly 250,000 DFT (B3LYP) energies for a large variety of con�figurations, obtained using MM3 direct-dynamics calculations at 500 K, 1000 K and 2500 K. The surfaces, based on Permutationally Invariant Polynomials (PIPs) and using both a many-body expansion approach and a fragmented-basis approach, produce precise �fits for energies and forces and also produce excellent out-of-sample agreement with direct DFT calculations for torsional and dihedral angle potentials. Going beyond precision, the PESs are used in molecular dynamics calculations that demonstrate the robustness of the PESs for a large range of conformations. The many-body PIPs PES, although more compute intensive than the fragmented-basis one, is directly transferable for other linear hydrocarbons.

10.26434/chemrxiv-2024-jrfxj

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Dynamics of the Simplest Chlorine Atom Reaction: An Experimental and Theoretical Study

Science (1996)

Michele Alagia Nadia Balucani Laura Cartechini Piergiorgio Casavecchia Eddy H. van Kleef

Angular distributions and time-of-flight spectra for the reaction Cl + H 2 → HCl + H obtained from a high-resolution, crossed-molecular beam experiment were compared to differential cross sections calculated by both converged quantum mechanical scattering and quasi-classical trajectory methods. Good agreement was found between the experimental results and each theoretical prediction. The results demonstrate that excellent agreement can be obtained between state-of-the-art simulations and experiments for the detailed dynamical properties of this prototype chlorine atom reaction.

Chlorine atom

Crossed molecular beam

Reaction dynamics

Molecular beam

10.1126/science.273.5281.1519

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Citations (107)

Workshop on combustion simulation databases for real transportation fuels

Jeffrey W. Hudgens Thomas C. Allison Donald R. Burgess Jeffery A Manion David M. Matheu

10.6028/nist.ir.7155

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