Effect of methyl trifluoride substitution on colorless transparency of polyimide: A DFT/TD-DFT study

Journal of Molecular Liquids, Journal Year: 2024, Volume and Issue: 411, P. 125691 - 125691

Published: Aug. 4, 2024

Language: Английский

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Interpretable Machine Learning Combined TD-DFT Calculations for the Study of Colorless Transparency Polyimides

Macromolecules, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 25, 2025

Language: Английский

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Studying the Thermodynamic Phase Stability of Organic–Inorganic Hybrid Perovskites Using Machine Learning

Molecules, Journal Year: 2024, Volume and Issue: 29(13), P. 2974 - 2974

Published: June 22, 2024

As an important photovoltaic material, organic–inorganic hybrid perovskites have attracted much attention in the field of solar cells, but their instability is one main challenges limiting commercial application. However, search for stable among thousands perovskite materials still faces great challenges. In this work, energy above convex hull values was predicted based on four different machine learning algorithms, namely random forest regression (RFR), support vector (SVR), XGBoost regression, and LightGBM to study thermodynamic phase stability perovskites. The results show that algorithm has a low prediction error can effectively capture key features related Meanwhile, Shapley Additive Explanation (SHAP) method used analyze algorithm. third ionization B element most critical feature stability, second electron affinity ions at X site, which are significantly negatively correlated with (Ehull). screening high site worthy priority. help us understand correlation between features, assist rapid discovery highly materials.

Language: Английский

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High-Throughput Screening and Prediction of Nucleophilicity of Amines Using Machine Learning and DFT Calculations

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(16), P. 6361 - 6368

Published: Aug. 8, 2024

Nucleophilic index (NNu) as a significant parameter plays crucial role in screening of amine catalysts. Indeed, the quantity and variety amines are extensive. However, only limited exhibit an NNu value exceeding 4.0 eV, rendering them potential nucleophiles chemical reactions. To address this issue, we proposed computational method to quickly identify with high values by using Machine Learning (ML) high-throughput Density Functional Theory (DFT) calculations. Our approach commenced training ML models exploration Molecular Fingerprint methods well development quantitative structure–activity relationship (QSAR) for well-known based on derived from DFT Utilizing explainable Shapley Additive Explanation plots, were able determine five critical substructures that significantly impact amine. The aforementioned conclusion can be applied produce cultivate 4920 novel hypothetical values. QSAR employed predict 259 amines, resulting identification exceptional (>4.55 eV). enhanced these validated One amine, H1, exhibits unprecedentedly 5.36 surpassing maximum (5.35 eV) observed well-established amines. research strategy efficiently accelerates discovery nucleophilicity predictions,

Language: Английский

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Accelerating the discovery of type Ⅱ photosensitizer: Experimentally validated machine learning models for predicting the singlet oxygen quantum yield of photosensitive molecule

Journal of Molecular Structure, Journal Year: 2024, Volume and Issue: 1321, P. 139850 - 139850

Published: Aug. 29, 2024

Language: Английский

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