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Related Concept Videos

Viscosity01:17

Viscosity

5.9K
When water is poured into a glass, it falls freely and quickly, whereas if honey or maple syrup is poured over a pancake, it flows slowly and sticks to the surface of the container. This difference in the flow of different kinds of liquids arises due to the fluid friction between the liquid layers and the liquid and the surrounding material. This property of fluids is called fluid viscosity. In this example, water has a lower viscosity than honey and maple syrup.
The SI unit of viscosity is...
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Viscosity of Fluid01:19

Viscosity of Fluid

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Viscosity measures the resistance a fluid offers to flow and deformation. It results from internal friction between layers of fluid moving relative to one another. Dynamic viscosity, denoted by the Greek letter mu (μ), quantifies the force needed to move one fluid layer over another. For Newtonian fluids like water and air, the relationship between the shearing stress and the rate of shearing strain is linear, meaning their viscosity remains constant regardless of the applied stress.
400
Surface Tension, Capillary Action, and Viscosity02:57

Surface Tension, Capillary Action, and Viscosity

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Surface Tension
The various IMFs between identical molecules of a substance are examples of cohesive forces. The molecules within a liquid are surrounded by other molecules and are attracted equally in all directions by the cohesive forces within the liquid. However, the molecules on the surface of a liquid are attracted only by about one-half as many molecules. Because of the unbalanced molecular attractions on the surface molecules, liquids contract to form a shape that minimizes the number...
27.8K
Stokes' Law01:20

Stokes' Law

1.3K
Viscous forces, like friction, are intermolecular forces that resist the relative motion of molecules over each other. When a solid body moves through a liquid, viscous forces drag it in the opposite direction. The force's magnitude depends on the solid's shape and size, as well as its speed and the liquid's coefficient of viscosity, density and temperature.
The expression for the force on a solid spherical object in a fluid is called Stokes' law. Stokes' law is valid only...
1.3K
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

221
Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
221
Accelerating Fluids01:17

Accelerating Fluids

1.0K
When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
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Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
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Advancing material property prediction: using physics-informed machine learning models for viscosity.

Alex K Chew1, Matthew Sender2, Zachary Kaplan1

  • 1Schrödinger, Inc., New York, 10036, USA.

Journal of Cheminformatics
|March 15, 2024
PubMed
Summary
This summary is machine-generated.

Integrating molecular dynamics (MD) descriptors into quantitative structure-property relationship (QSPR) models significantly improves viscosity predictions for materials, especially with limited data. This approach enhances accuracy and interpretability in materials science machine learning.

Keywords:
Classical molecular dynamics simulationsMachine learningOrganic moleculesPhysical propertiesQuantitative structure–property relationshipsViscosity

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning in Materials

Background:

  • Physics-based models struggle with accurate computation of material properties like viscosity.
  • Data-driven machine learning (ML) models face challenges in materials science due to limited data availability.
  • Accurate prediction of viscosity is crucial for understanding liquid systems and material behavior.

Purpose of the Study:

  • To enhance the accuracy and interpretability of ML models for predicting material properties.
  • To integrate physics-informed descriptors from molecular dynamics (MD) simulations into quantitative structure-property relationship (QSPR) models.
  • To accurately predict temperature-dependent viscosities of small organic molecules using QSPR models.

Main Methods:

  • Curated a dataset of over 4000 small organic molecule viscosities from scientific literature and databases.
  • Developed descriptor-based and graph neural network QSPR models incorporating MD simulation descriptors.
  • Utilized feature importance tools to identify key predictive descriptors.

Main Results:

  • Incorporating MD descriptors significantly improved viscosity prediction accuracy, particularly for datasets with fewer than 1000 data points.
  • Intermolecular interactions, captured by MD descriptors, were identified as the most critical features for viscosity prediction.
  • The developed QSPR models accurately predicted the inverse relationship between viscosity and temperature for six battery-relevant solvents, including unseen ones.

Conclusions:

  • Integrating MD descriptors into QSPR models is an effective strategy for improving prediction accuracy of challenging material properties.
  • This hybrid approach overcomes limitations of physics-based models and data scarcity in machine learning for materials science.
  • The study demonstrates the utility of MD-enhanced QSPR for predicting temperature-dependent viscosity in liquid systems.