Predicting the enthalpy of formation of energetic molecules <i>via</i> conventional machine learning and GNN

Zhang, Di; Chu, Qingzhao; Chen, Dongping

doi:10.1039/d3cp05490j

Phys. Chem. Chem. Phys.

2024

DOI: 10.1039/d3cp05490j

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Predicting the enthalpy of formation of energetic molecules via conventional machine learning and GNN

Di Zhang,

Qingzhao Chu,

Dongping Chen

Abstract: Machine learning (ML) provides a promising method for efficiently and accurately predicting molecular properties. Using ML models to predict the enthalpy of formation of energetic molecules helps in fast screening...

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Cited by 3 publications

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Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO2-capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO2-capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO2-capture property prediction and design optimization.

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Predicting the enthalpy of formation of energetic molecules via conventional machine learning and GNN

Abstract: Machine learning (ML) provides a promising method for efficiently and accurately predicting molecular properties. Using ML models to predict the enthalpy of formation of energetic molecules helps in fast screening...

Cited by 3 publications

References 55 publications

Machine learning predictions of thermochemical properties for aliphatic carbon and oxygen species

Machine learning predictions of thermochemical properties for aliphatic carbon and oxygen species

(R)evolution of Viruses: Introduction to biothermodynamics of viruses

Comparative study of machine learning techniques for post-combustion carbon capture systems

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