Computational Studies of Auto-Active van der Waals Interaction Molecules on Ultra-Thin Black-Phosphorus Film

Laref, S.; Wang, Bin; Gao, Xin; Gojobori, Takashi

doi:10.3390/molecules28020681

Cited by 6 publications

(4 citation statements)

References 65 publications

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“…The relaxed geometries are further used for creating a black phosphorus sheet for loading antiviral molecules that characterize the drug-BP based nanocarrier. Accordingly, the optimized ML structures (MLFF-EB, MLFF-FP, and MLFF-FP_EB, at T = 0 K) are in good agreement with the interaction energies and van der Waals energies found by means of DFT calculations [45]. For more information, please see Tables S1 and S2, respectively.…”

Section: Discussionsupporting

confidence: 78%

“…In the second experiment, the main key objective was to predict and improve the non-bond energy for MLFF-EB, MLFF-FP, and MLFF-FP_EB based on seven input variables (i.e., total kinetic energy, angle energy, torsion energy, inversion energy, van der Waals energy, pressure, and temperature) related to ab initio results [45]. Towards this end, we constructed four machine learning models (BT, SVR, GPR, and RT) based on a 5-fold crossvalidation with the training dataset.…”

Section: Non-bond Energy Predictionmentioning

confidence: 99%

“…In general, the drug will have a larger release rate beyond 350 K, and it might be due to the height's thermal deviation at a larger temperature. In the ex vivo evaluation process, the phosphorene-loaded drugs can be used as an anti-defected cell drug that has a pertinent act in the thermal therapeutic [45]. The temperature could upturn to around 450 K within a few minutes.…”

Section: Thermotherapy Propertiesmentioning

confidence: 99%

See 2 more Smart Citations

Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights

et al. 2023

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Favipiravir (FP) and Ebselen (EB) belong to a broad range of antiviral drugs that have shown active potential as medications against many viruses. Employing molecular dynamics simulations and machine learning (ML) combined with van der Waals density functional theory, we have uncovered the binding characteristics of these two antiviral drugs on a phosphorene nanocarrier. Herein, by using four different machine learning models (i.e., Bagged Trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Regression Trees (RT)), the Hamiltonian and the interaction energy of antiviral molecules in a phosphorene monolayer are trained in an appropriate way. However, training efficient and accurate models for approximating the density functional theory (DFT) is the final step in using ML to aid in the design of new drugs. To improve the prediction accuracy, the Bayesian optimization approach has been employed to optimize the GPR, SVR, RT, and BT models. Results revealed that the GPR model obtained superior prediction performance with an R2 of 0.9649, indicating that it can explain 96.49% of the data’s variability. Then, by means of DFT calculations, we examine the interaction characteristics and thermodynamic properties in a vacuum and a continuum solvent interface. These results illustrate that the hybrid drug is an enabled, functionalized 2D complex with vigorous thermostability. The change in Gibbs free energy at different surface charges and temperatures implies that the FP and EB molecules are allowed to adsorb from the gas phase onto the 2D monolayer at different pH conditions and high temperatures. The results reveal a valuable antiviral drug therapy loaded by 2D biomaterials that may possibly open a new way of auto-treating different diseases, such as SARS-CoV, in primary terms.

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Section: Discussionsupporting

confidence: 78%

Section: Non-bond Energy Predictionmentioning

confidence: 99%

Section: Thermotherapy Propertiesmentioning

confidence: 99%

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Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights

et al. 2023

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“…In the last 10-20 years, the number of studies that have benchmarked a specific property calculated from DFT or used a more advanced computational method has been increasing [13,[16][17][18][19][20][21][22][23][24][25][26][27][28][29]. However, very few of them have focused on the complex issue of predicting the equilibrium composition systematically.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking First-Principles Reaction Equilibrium Composition Prediction

et al. 2023

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The availability of thermochemical properties allows for the prediction of the equilibrium compositions of chemical reactions. The accurate prediction of these can be crucial for the design of new chemical synthesis routes. However, for new processes, these data are generally not completely available. A solution is the use of thermochemistry calculated from first-principles methods such as Density Functional Theory (DFT). Before this can be used reliably, it needs to be systematically benchmarked. Although various studies have examined the accuracy of DFT from an energetic point of view, few studies have considered its accuracy in predicting the temperature-dependent equilibrium composition. In this work, we collected 117 molecules for which experimental thermochemical data were available. From these, we constructed 2648 reactions. These experimentally constructed reactions were then benchmarked against DFT for 6 exchange–correlation functionals and 3 quality of basis sets. We show that, in reactions that do not show temperature dependence in the equilibrium composition below 1000 K, over 90% are predicted correctly. Temperature-dependent equilibrium compositions typically demonstrate correct qualitative behavior. Lastly, we show that the errors are equally caused by errors in the vibrational spectrum and the DFT electronic ground state energy.

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Hydrogen‐Bonded Supramolecular Network Enabled Gentle Reprogramming of Liquid Crystal Elastomer toward Evolutionary Robot

Wang,

Si,

Han

et al. 2024

Angewandte Chemie

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In nature, many organisms augment chances of survival by reprogramming their structures to evolving environment, among which sea squirts being a prime example. Such reprogramming has been demonstrated in liquid crystal elastomer (LCE) actuator assembled with heat assistance. However, the required temperature being higher than the actuation temperature limits its application. Here, we reported a hydrogen‐bonded supramolecular network LCE to construct soft modular and reprogrammable actuator by assembling with a gentle heat treatment. Leveraging the Michael addition reaction, we introduced hydrogen bonding to the LCE matrix with functionalized pyridine monomers. Experimental and molecular dynamics modeling proved the efficient dynamic hydrogen bond exchange at 60°C, significantly lower than the actuating temperature of the LCE. This gave rise to the reversible and robust adhesion of the same collection of LCE modules capable of being built into different bilayers and performing various morphing upon a short thermal stimulation. Therefore, we demonstrated that these comparatively weak cross‐links enabled reconfiguration of the LCE actuator. With the developed hydrogen‐bonding LCEs, we built proof‐of‐concept modular reprogrammable robot, performing crawling, sailing, and microcircuit repair tasks. This bioinspired and efficient method for evolutionary LCE robot offers a viable path for further development of intelligent actuators sustainable in complex environments.

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Computational Studies of Auto-Active van der Waals Interaction Molecules on Ultra-Thin Black-Phosphorus Film

Cited by 6 publications

References 65 publications

Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights

Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights

Benchmarking First-Principles Reaction Equilibrium Composition Prediction

Hydrogen‐Bonded Supramolecular Network Enabled Gentle Reprogramming of Liquid Crystal Elastomer toward Evolutionary Robot

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