Leveraging machine learning for accelerated materials innovation in lithium-ion battery: a review

Journal of Energy Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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A critical methodological revisit on group-contribution based property prediction of ionic liquids with machine learning

Chemical Engineering Science, Год журнала: 2024, Номер 298, С. 120395 - 120395

Опубликована: Июнь 17, 2024

Язык: Английский

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Quantitative Structure–Property Relationships (QSPR) for Materials Science

Challenges and advances in computational chemistry and physics, Год журнала: 2025, Номер unknown, С. 61 - 79

Опубликована: Янв. 1, 2025

Язык: Английский

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Advanced transformer models for structure-property relationship predictions of ionic liquid melting points

Chemical Engineering Journal, Год журнала: 2024, Номер 503, С. 158578 - 158578

Опубликована: Дек. 12, 2024

Язык: Английский

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Insight into the Mechanism of Machine Learning Models for Predicting Ionic Liquids Toxicity Based on Molecular Structure Descriptors

ACS Sustainable Chemistry & Engineering, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 21, 2024

The development and application of functionalized ionic liquids (ILs) are currently hot topics in chemical engineering. However, research on ILs toxicity is significantly lagging behind studies their physical properties applications. This study begins with the construction model, utilizing three types descriptors to quantify structures developing four machine learning (ML) models for predicting Daphnia magna. Guttmann coefficients used evaluate diversity structures. Feature engineering employed optimize inputs quantitative structure–activity relationship (QSAR) models, enhancing ability capture between toxicity. Grid search cross-validation ensure model robustness prevent overfitting. Results indicate that random forest based RDKit performs best (R2 = 0.975, RMSE 0.222). SHAP analysis identifies key molecular features contributing toxicity, revealing substructures around carbon atoms crucial while containing oxygen can reduce These findings offer insights designing low-toxicity, environmentally friendly highlight value green chemistry sustainability research.

Язык: Английский

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Molecular insight into the dissolution-degradation process of cellulose bunch in ionic liquids

Journal of Molecular Liquids, Год журнала: 2024, Номер unknown, С. 126191 - 126191

Опубликована: Окт. 1, 2024

Язык: Английский

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Machine learning boosted eutectic solvent design for CO₂ capture with experimental validation

AIChE Journal, Год журнала: 2024, Номер 71(2)

Опубликована: Окт. 18, 2024

Abstract Although eutectic solvents (ESs) have garnered significant attention as promising for carbon dioxide (CO 2 ) capture, systematic studies on discovering novel ESs linking machine learning (ML) and experimental validation are scarce. For the reliable prediction of CO ‐in‐ES solubility, ensemble ML modeling based random forest extreme gradient boosting with inputs COSMO‐RS derived molecular descriptors is rigorously performed, which an extensive solubility database 2438 data points in 162 involving 106 ES systems collected. With best‐performing model obtained, solubilities 4735 combinations components first predicted estimating their potential capture. The top‐ranked candidate subsequently evaluated by examining environmental health safety properties individual assessing operating window solid–liquid equilibrium (SLE) prediction. Three most finally retained, thoroughly studied SLE absorption experiments.

Язык: Английский

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Encapsulated ionic liquids: A comprehensive review of production methods and potential applications

Chemical Engineering Journal, Год журнала: 2024, Номер unknown, С. 159039 - 159039

Опубликована: Дек. 1, 2024

Язык: Английский

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Effect of structure and interaction on physicochemical properties of new [Emim][BF3X] complex anion ionic liquids studied by quantum chemistry

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 6, 2024

One of the key challenges in industrial application ionic liquids (ILs) is their extreme characteristics, such as viscosity, glass-transition temperatures and conductivity. Understanding relationship between ILs structure physicochemical propertie a crucial aspect directed design with good properties, which prerequisite for successful implementation processes. In this work, high-level quantum-chemical research four pairs liquids, [Emim][X] [Emim][BF₃X] (X=CH₃SO₃, EtSO₄, HSO₄, Tos), was performed, to provide new insight into property variances at molecular level. The result shows that overall stability contributed hydrogen bonding network protons C-H N-H cation oxygen atoms anion, well fluorine atoms. nature strength interionic interaction were measured via molecules analysis sobEDAw method results suggested BF₃ could waning ion pairs. Moreover, close relation binding energies properties established: weaker interaction, lower viscosity glass-transition, higher

Язык: Английский

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Effect of structure and interaction on physicochemical properties of new [Emim][BF3X] complex anion ionic liquids studied by quantum chemistry

Journal of Molecular Modeling, Год журнала: 2024, Номер 30(12)

Опубликована: Ноя. 18, 2024

Язык: Английский

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