Graph theory for automatic structural recognition in molecular dynamics simulations

Bougueroua, Sana; Spezia, Riccardo; Pezzotti, Simone; Vial, Sandrine; Quessette, Franck; Barth, Dominique; Gaigeot, Marie‐Pierre

doi:10.1063/1.5045818

Cited by 27 publications

(57 citation statements)

References 27 publications

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“…Progress towards machine learning (ML) methods for environmental pollutant analysis has been explored for specific, targeted applications. 9,[15][16][17] Generalizable functional group ML models would increase the utility of FTIR sample screening in environmental and other chemistry applications. 18,19 In this study, we investigate the implementation of convolutional neural networks (CNNs) 20 to identify functional groups present in FTIR spectra.…”

Section: Introductionmentioning

confidence: 99%

Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

et al. 2021

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Fourier Transform Infrared Spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using Convolutional Neural Networks (CNNs) to identify the presence of functional groups in gas phase FTIR spectra. The ML models will reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas phase organic molecules within the NIST spectral database and transform the data into images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that inference in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method. File list (2) download file view on ChemRxiv ML functional group ID in FTIR spectra.docx (545.84 KiB) download file view on ChemRxiv ML functional group ID in FTIR spectra.pdf (645.08 KiB) Functional group identification for FTIR spectra using image-based machine learning models

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Section: Introductionmentioning

confidence: 99%

Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

et al. 2021

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“…As detailed below, BBFS is based on the adjacency matrix, a Graph Theory object that has been employed in other successful automated methods like the one developed by Zimmerman [16]. Similar ideas have also been recently employed to analyze changes in conformations occurring in MD simulations [74].…”

Section: Methodsmentioning

confidence: 99%

A Trajectory-Based Method to Explore Reaction Mechanisms

2018

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The tsscds method, recently developed in our group, discovers chemical reaction mechanisms with minimal human intervention. It employs accelerated molecular dynamics, spectral graph theory, statistical rate theory and stochastic simulations to uncover chemical reaction paths and to solve the kinetics at the experimental conditions. In the present review, its application to solve mechanistic/kinetics problems in different research areas will be presented. Examples will be given of reactions involved in photodissociation dynamics, mass spectrometry, combustion chemistry and organometallic catalysis. Some planned improvements will also be described.

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Section: Methodsmentioning

confidence: 99%

A Trajectory-Based Method to Explore Reaction Mechanisms

Vázquez¹,

Otero²,

Martı́nez-Núñez³

2018

Preprint

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The method tsscds, recently developed in our group, discovers chemical reaction mechanisms with minimal human intervention. It employs accelerated molecular dynamics, spectral graph theory, statistical rate theory and stochastic simulations to uncover chemical reaction paths and to solve the kinetics at the experimental conditions. In the present review, its application to solve mechanistic/kinetics problems in different research areas will be presented. Examples will be given of reactions involved in photodissociation dynamics, mass spectrometry, combustion chemistry and organometallic catalysis. Some planned improvements will also be described. The source code can be downloaded from: http://forge.cesga.es/wiki/g/tsscds/HomePage

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Graph theory for automatic structural recognition in molecular dynamics simulations

Cited by 27 publications

References 27 publications

Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

A Trajectory-Based Method to Explore Reaction Mechanisms

A Trajectory-Based Method to Explore Reaction Mechanisms

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