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Related Concept Videos

Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
Acceleration Vectors01:30

Acceleration Vectors

In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h due...
Accelerating Fluids01:17

Accelerating Fluids

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:
Angular Velocity and Acceleration01:11

Angular Velocity and Acceleration

We previously discussed angular velocity for uniform circular motion, however not all motion is uniform. Envision an ice skater spinning with their arms outstretched; when they pull their arms inward, their angular velocity increases. Additionally, think about a computer's hard disk slowing to a halt as the angular velocity decreases. The faster the change in angular velocity, the greater the angular acceleration. The instantaneous angular acceleration is defined as the derivative of angular...
Average Acceleration01:30

Average Acceleration

The importance of understanding acceleration spans our day-to-day experiences, as well as the vast reaches of outer space and the tiny world of subatomic physics. In everyday conversation, to accelerate means to speed up. For instance, we are familiar with the acceleration of our car; the harder we apply our foot to the gas pedal, the faster we accelerate. The greater the acceleration, the greater the change in velocity over a given time. Acceleration is widely seen in experimental physics. In...
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...

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Related Experiment Videos

Accelerating materials property predictions using machine learning.

Ghanshyam Pilania1, Chenchen Wang, Xun Jiang

  • 1Department of Materials Science and Engineering, University of Connecticut, 97 North Eagleville Road, Storrs, Connecticut 06269.

Scientific Reports
|October 1, 2013
PubMed
Summary
This summary is machine-generated.

Machine learning models trained on quantum mechanical computations can rapidly predict material properties. This approach accelerates the discovery of new materials by learning from existing data and chemical similarity.

Related Experiment Videos

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Data Science

Background:

  • Materials discovery is often slow and resource-intensive.
  • Leveraging existing data and computational insights can accelerate the process.
  • Machine learning offers powerful tools for pattern recognition and prediction.

Purpose of the Study:

  • To develop and demonstrate a machine learning framework for predicting material properties.
  • To establish a general formalism for mapping material attributes to properties.
  • To accelerate the discovery of novel materials for specific applications.

Main Methods:

  • Employed machine learning models trained on quantum mechanical computation data.
  • Utilized concepts of chemical similarity to enhance predictions.
  • Developed decision rules based on chemo-structural attributes and electronic charge density.

Main Results:

  • Achieved efficient and accurate prediction of diverse material properties.
  • Demonstrated the efficacy of fingerprints based on chemo-structural information.
  • Showcased the predictive power of fingerprints derived from electronic charge density distributions.
  • Validated the approach using one-dimensional chain systems.

Conclusions:

  • Machine learning combined with quantum mechanics and chemical similarity enables rapid property prediction.
  • Chemo-structural and electronic charge density fingerprints are effective for ultra-fast predictions.
  • This paradigm significantly accelerates the exploration of chemical space for materials discovery.