53 results on '"Le, Tu C."'
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2. Rational Atom Substitution to Obtain Efficient, Lead‐Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations
3. Rational Atom Substitution to Obtain Efficient, Lead‐Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations
4. Liquid Metal Alloy Catalysis – Challenges and Prospects
5. Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors
6. Carbon Fibre Reinforced Polymer Materials for Antennas and Microwave Technologies
7. Synthesis of Layered Lead-Free Perovskite Nanocrystals with Precise Size and Shape Control and Their Photocatalytic Activity
8. Fabrication of Zein‐Based Fibrous Scaffolds for Biomedical Applications—A Review
9. Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts
10. Applications in Materials Science
11. Characterising a protic ionic liquid library with applied machine learning algorithms
12. Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture
13. Membrane Permeating Macrocycles: Design Guidelines from Machine Learning
14. Computational Approaches
15. Contributors
16. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
17. Machine learning investigation of viscosity and ionic conductivity of protic ionic liquids in water mixtures
18. Prediction of O2/N2 Selectivity in Metal–Organic Frameworks via High-Throughput Computational Screening and Machine Learning
19. Predicting heat release properties of flammable fiber-polymer laminates using artificial neural networks
20. Design of Lipid-Based Nanocarriers via Cation Modulation of Ethanol-Interdigitated Lipid Membranes
21. Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts
22. Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations
23. A systematic comparison of the structural and dynamic properties of commonly used water models for molecular dynamics simulations
24. A systematic comparison of the structural and dynamic properties of commonly used water models for molecular dynamics simulations
25. Internet of Things and autonomous control for vertical cultivation walls towards smart food growing: A review
26. Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy
27. Potent In Vitro Peptide Antagonists of the Thrombopoietin Receptor as Potential Myelofibrosis Drugs
28. Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids
29. Janus particles: recent advances in the biomedical applications
30. Machine Learning Approaches for Further Developing the Understanding of the Property Trends Observed in Protic Ionic Liquid Containing Solvents
31. Using Machine Learning To Predict the Self-Assembled Nanostructures of Monoolein and Phytantriol as a Function of Temperature and Fatty Acid Additives for Effective Lipid-Based Delivery Systems
32. Quantitative design rules for protein-resistant surface coatings using machine learning
33. Toward Cell Membrane Biomimetic Lipidic Cubic Phases: A High-Throughput Exploration of Lipid Compositional Space
34. Manipulating the Ordered Nanostructure of Self-Assembled Monoolein and Phytantriol Nanoparticles with Unsaturated Fatty Acids
35. Correction to “Modeling the Influence of Fatty Acid Incorporation on Mesophase Formation in Amphiphilic Therapeutic Delivery Systems”
36. Corrigendum: Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem Activity Cliffs, and QSAR
37. Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR
38. Discovery and Optimization of Materials Using Evolutionary Approaches
39. An Experimental and Computational Approach to the Development of ZnO Nanoparticles that are Safe by Design
40. Robust Prediction of Personalized Cell Recognition from a Cancer Population by a Dual Targeting Nanoparticle Library
41. A Bright Future for Evolutionary Methods in Drug Design
42. Illuminating Flash Point: Comprehensive Prediction Models
43. In Meso Crystallization: Compatibility of Different Lipid Bicontinuous Cubic Mesophases with the Cubic Crystallization Screen in Aqueous Solution
44. Aqueous Solubility Prediction: Do Crystal Lattice Interactions Help?
45. Predicting the Effect of Lipid Structure on Mesophase Formation during in Meso Crystallization
46. Predicting the Complex Phase Behavior of Self-Assembling Drug Delivery Nanoparticles
47. Correction to Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds
48. Computational Modeling and Prediction of the Complex Time-Dependent Phase Behavior of Lyotropic Liquid Crystals under in Meso Crystallization Conditions
49. Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds
50. The effect of interbranch spacing on structural and rheological properties of hyperbranched polymer melts
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