Predicting second virial coefficients of organic and inorganic compounds using Gaussian process regression

Cretu, Miruna T.; Pérez‐Ríos, Jesús

doi:10.1039/d0cp05509c

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…Two disconnected fields, ab initio methods and cluster integrals description, 173 have been recomposing recently 174 with the support of Symmetry-aware ML models, notably Gaussian Processes (GP) 175 and active learning from the most informative instances to interpolate accurate configurational energies through ab initio N-body potential energy surfaces (PESs). GP interpolation can be used in Path-Integral Monte Carlo computation of Mayer integrals, 176 which maps a system of N Boltzmann particles onto a system of N ring polymers with P beads or points.…”

Section: From Mixture To Propertymentioning

confidence: 99%

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

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Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the ’recomposition’ of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure–Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

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Section: From Mixture To Propertymentioning

confidence: 99%

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

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“…Gradient boosting regression has been successfully applied to predict BHs in Diels–Alder reactions, and the reactivity of transition metal complexes . Similarly, GP has shown a great performance in complex potential energy surface fittings, − predicting spectroscopic constants of diatomic molecules , and second virial coefficients of organic and inorganic compounds . The selected SQM model was PM7, which is overall the most accurate method implemented in MOPAC2016 .…”

Section: Introductionmentioning

confidence: 99%

“… 15 Similarly, GP has shown a great performance in complex potential energy surface fittings, 34 − 37 predicting spectroscopic constants of diatomic molecules 38 , 39 and second virial coefficients of organic and inorganic compounds. 40 The selected SQM model was PM7, 18 which is overall the most accurate method implemented in MOPAC2016. 41 To fully exploit the SQM calculations, several input features are constructed from the electronic and structural properties of reactant, TSs, and products.…”

Section: Introductionmentioning

confidence: 99%

Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations

García-Andrade¹,

Tahoces

Pérez‐Ríos

et al. 2023

J. Phys. Chem. A

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Different machine learning (ML) models are proposed in the present work to predict density functional theory-quality barrier heights (BHs) from semiempirical quantum mechanical (SQM) calculations. The ML models include a multitask deep neural network, gradient-boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors are similar to those of previous models considering the same number of data points. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in combustion chemistry or in astrochemistry. Finally, our results show that 70% of the features with the highest impact on model output are bespoke predictors. This custom-made set of predictors could be employed by future Δ-ML models to improve the quantitative prediction of other reaction properties.

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Analysis of p, T, ρ-data for all fluid states via comparison with a transcribed Van der Waals equation

Mayrhuber¹

2021

Chemical Thermodynamics and Thermal Analysis

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Predicting second virial coefficients of organic and inorganic compounds using Gaussian process regression

Abstract: Intuitive and accessible molecular features are used to predict the temperature-dependent second virial coefficient of organic and inorganic compounds with Gaussian process regression.

Cited by 7 publications

References 30 publications

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations

Analysis of p, T, ρ-data for all fluid states via comparison with a transcribed Van der Waals equation

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