Investigation of photovoltaic performance of lead-free CsSnI3-based perovskite solar cell with different hole transport layers: First Principle Calculations and SCAPS-1D Analysis

Solar Energy, Journal Year: 2022, Volume and Issue: 249, P. 163 - 173

Published: Dec. 1, 2022

Language: Английский

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Easy and fast prediction of green solvents for small molecule donor-based organic solar cells through machine learning

Physical Chemistry Chemical Physics, Journal Year: 2023, Volume and Issue: 25(15), P. 10417 - 10426

Published: Jan. 1, 2023

Solubility plays a critical role in many aspects of research (drugs to materials). parameters are very useful for selecting appropriate solvents/non-solvents various applications. In the present study, Hansen solubility predicted using machine learning. More than 40 models tried search best model. Molecular descriptors and fingerprints used as inputs get comparative view. Machine learning trained molecular have shown higher prediction ability model fingerprints. their potential be easy fast compared density functional theory (DFT)/thermodynamic approach. creates "black box" connection properties. Therefore, minimal computational cost is required. With help best-trained model, green solvents selected small molecule donors that organic solar cells. Our introduced framework can select cells an way.

Language: Английский

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Molecular level understanding of the chalcogen atom effect on chalcogen-based polymers through electrostatic potential, non-covalent interactions, excited state behaviour, and radial distribution function

Polymer Chemistry, Journal Year: 2022, Volume and Issue: 13(42), P. 5993 - 6001

Published: Jan. 1, 2022

Multi-dimensional modelling was used to study the effect of chalcogen atoms on non-covalent interactions, structural and electronic properties polymer materials. Their bulk were also studied at molecular level.

Language: Английский

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Investigation of the cationic resin as a potential adsorbent to remove MR and CV dyes: Kinetic, equilibrium isotherms studies and DFT calculations

Journal of Molecular Structure, Journal Year: 2023, Volume and Issue: 1278, P. 134849 - 134849

Published: Jan. 5, 2023

Language: Английский

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Machine learning assisted designing of Y-series small molecule acceptors: Library generation and property prediction

Journal of Photochemistry and Photobiology A Chemistry, Journal Year: 2024, Volume and Issue: 453, P. 115670 - 115670

Published: April 12, 2024

Language: Английский

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Chemical similarity-based design of materials for organic solar cells: Visualizing the generated chemical space of polymers

Materials Today Communications, Journal Year: 2024, Volume and Issue: 38, P. 108403 - 108403

Published: Feb. 17, 2024

Language: Английский

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Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm

SmartMat, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 9, 2025

ABSTRACT Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, models. They can target various definitions, distributions, correlations of concerned physical parameters given polymer systems, have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages building quantitative multivariate largely enhanced the capability scientific understanding discoveries, thus facilitating mechanism exploration, prediction, high‐throughput screening, optimization, rational inverse designs. This article summarizes representative progress recent two decades focusing on design, preparation, application, sustainable development materials based exploration key composition–process–structure–property–performance relationship. The integration both data‐driven insights through ML deepen fundamental discover novel is categorically presented. Despite construction application robust models, strategies algorithms deal with variant tasks science still rapid growth. challenges prospects then We believe that innovation will thrive along approaches, from efficient design applications.

Language: Английский

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Machine Learning-Assisted Prediction of the Biological Activity of Aromatase Inhibitors and Data Mining to Explore Similar Compounds

ACS Omega, Journal Year: 2022, Volume and Issue: 7(51), P. 48139 - 48149

Published: Dec. 13, 2022

Designing molecules for drugs has been a hot topic many decades. However, it is hard and expensive to find new molecule. Thus, the cost of final drug also increased. Machine learning can provide fastest way predict biological activity druglike molecules. In present work, machine models are trained prediction aromatase inhibitors. Data was collected from literature. Molecular descriptors calculated be used as independent features model training. The results showed that R2 values linear regression, random forest gradient boosting bagging regression 0.58, 0.84, 0.77, 0.80, respectively. Using these models, possible in short period time at reasonable cost. Furthermore, Tanimoto similarity analysis, well chemical database mined search similar Nonetheless, this study provides framework repurposing other effective prevent cancer.

Language: Английский

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Virtual screening and library enumeration of new hydroxycinnamates based antioxidant compounds: A complete framework

Journal of Saudi Chemical Society, Journal Year: 2023, Volume and Issue: 27(4), P. 101670 - 101670

Published: June 7, 2023

Designing of molecules for drugs is important topic from many decades. The search new very hard, and it expensive process. Computer assisted framework can provide the fastest way to design screen drug-like compounds. In present work, a multidimensional approach introduced designing screening antioxidant Antioxidants play crucial role in ensuring that body's oxidizing reducing species are kept proper balance, minimizing oxidative stress. Machine learning models used predict activity. Three hydroxycinnamates selected as standard antioxidants. Similar compounds searched ChEMBL database using chemical structural similarity method. libraries generated evolutionary New also designed automatic decomposition construction building blocks. activity all predicted machine models. space envisioned t-distributed stochastic neighbor embedding (t-SNE) Best shortlisted, their synthetic accessibility further facilitate experimental chemists. between studied fingerprints heatmap.

Language: Английский

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Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization

Diagnostics, Journal Year: 2023, Volume and Issue: 13(12), P. 2038 - 2038

Published: June 12, 2023

In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due its widespread occurrence around the world. Diabetes is just one of several diseases for techniques can be in diagnosis, prognosis, and assessment procedures.In this paper, we propose a new approach boosting classification based on metaheuristic optimization algorithm. The proposed proposes feature selection algorithm dynamic Al-Biruni earth radius dipper-throated (DBERDTO). selected features are then classified using random forest classifier with parameters optimized DBERDTO.The methodology evaluated compared recent methods models prove efficiency superiority. overall accuracy achieved by 98.6%. On other hand, statistical tests conducted assess significance difference analysis variance (ANOVA) Wilcoxon signed-rank tests.The results these confirmed superiority methods.

Language: Английский

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