Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network

Mohan, Mood; Demerdash, Omar; Simmons, Blake A.; Smith, Jeremy C.; Kidder, Michelle K.; Singh, Seema

doi:10.1039/d2gc04425k

Cited by 32 publications

(37 citation statements)

References 65 publications

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“…However, testing all these combinations in real-life experiments is practically impossible. 24 Consequently, a surging interest in developing computational models arises, aiming to predict CO 2 solubilities within DESs. These models present a cost-effective and time-efficient approach to identifying efficacious solvent systems for carbon capture and utilization.…”

Section: Introductionmentioning

confidence: 99%

Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents

Salahshoori,

Baghban,

Yazdanbakhsh

2023

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents

Salahshoori,

Baghban,

Yazdanbakhsh

2023

RSC Adv.

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“…The σ-profile binning integral interval length of 0.5 e/nm 2 is standard in the literature, and we have merged all zero sigma fractions into a single fraction (for example: S1, S6, S7, and S12). Similar σ-profile integration intervals were clearly described elsewhere. − The explicit interactions between cations and anions are not calculated due to the high computational cost. We calculated a binned probability of polarized charge at the molecular surface (i.e., the COSMO-RS-derived sigma profile) that we hypothesized is likely to implicitly capture the propensity for certain intermolecular interactions, either among anion or cation molecules or between an anion and cation.…”

Section: Methodsmentioning

confidence: 98%

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Mohan

Smith

Demerdash

et al. 2023

ACS Sustainable Chem. Eng.

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Ionic liquids (ILs) have unique solvent properties and have thus garnered significant interest. However, exhaustive experimental determination of the physicochemical properties of ILs is unrealistic due to the large structural diversity of anions and cations, their high cost, the requirements of elevated temperature and pressure, and the time required. To circumvent these experimental costs, computational approaches to accurately calculate these properties have emerged. In the present study, we present a demonstration of two machine learning (ML) models for the prediction of two critical IL physical properties, the surface tension and the speed of sound, across a wide range of temperatures and pressures. The models make use of molecular descriptors derived from the COSMO-RS, a quantum chemical-based model. The ML models show excellent agreement with experimental observations, with an R 2 value of 0.96–0.99 and RMSE of 1.71 mN/m and 16.12 m/s for the surface tension and speed of sound, respectively. This work paves the way for the development of COSMO-RS-informed ML models for the prediction of IL properties which can help to further optimize and accelerate technology development for ILs.

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“…Certain modeling techniques, which have proven effective for pure molecule systems, may yield considerable prediction inaccuracies when confronted with the challenges of characterizing intermolecular interactions in mixtures. More specifically, among prevailing QSPR studies for ES, ,,, their strategies of mixture representation are predominantly merging the specialized molecular descriptors or S σ‑profile descriptors together. However, from an intermolecular interaction standpoint, such a method of ES representation may struggle to yield predictive models with tangible practical applicability.…”

Section: Representative Applications Of Computer-aided Il Screening A...mentioning

confidence: 99%

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

Song,

Chen,

Cheng

et al. 2023

Chem. Rev.

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The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure−property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.

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Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network

Abstract: Carbon dioxide (CO2) emissions from fossil fuel combustion are a significant cause of greenhouse gas, contributing in a major way to global warming and climate change. Carbon dioxide capture and...

Cited by 32 publications

References 65 publications

Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents

Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

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