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Gauss's Law: Problem-Solving01:10

Gauss's Law: Problem-Solving

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Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
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Gauss's Law01:07

Gauss's Law

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If a closed surface does not have any charge inside where an electric field line can terminate, then the electric field line entering the surface at one point must necessarily exit at some other point of the surface. Therefore, if a closed surface does not have any charges inside the enclosed volume, then the electric flux through the surface is zero. What happens to the electric flux if there are some charges inside the enclosed volume? Gauss's law gives a quantitative answer to this...
7.0K
Gauss's Law in Dielectrics01:17

Gauss's Law in Dielectrics

4.2K
Consider a polar dielectric placed in an external field. In such a dielectric, opposite charges on adjacent dipoles neutralize each other, such that the net charge within the dielectric is zero. When a polar dielectric is inserted in between the capacitor plates, an electric field is generated due to the presence of net charges near the edge of the dielectric and the metal plates interface. Since the external electrical field merely aligns the dipoles, the dielectric as a whole is neutral. An...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Behavioral Genetics and Its Designs01:23

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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Updated: May 20, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material Designs.

Shin Kiyohara1, Yu Kumagai1

  • 1Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan.

The Journal of Physical Chemistry Letters
|May 19, 2025
PubMed
Summary
This summary is machine-generated.

Deep kernel learning (DKL) enhances Bayesian optimization (BO) for materials discovery by combining neural networks with Gaussian processes (GPs). DKL shows improved efficiency over standard GPs in exploring material properties, paving the way for faster material exploration.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Machine learning (ML) is crucial for accelerating materials discovery.
  • Bayesian optimization (BO) using Gaussian processes (GPs) is a common approach for material exploration.
  • GP-based BO efficiency is limited by the need for manual feature engineering.

Purpose of the Study:

  • To investigate the effectiveness of deep kernel learning (DKL) combined with BO for materials discovery.
  • To compare the performance of DKL-based BO against standard GP-based BO.

Main Methods:

  • Application of deep kernel learning (DKL), integrating neural networks with GPs, to Bayesian optimization (BO).
  • Evaluation of DKL model efficiency on oxide datasets (922 entries) for band gaps, dielectric constants, and electron effective masses.
  • Assessment of DKL performance on hybrid organic-inorganic perovskite alloy band gaps (610 entries) and Curie temperature prediction for 4560 alloys.

Main Results:

  • DKL-based BO demonstrated comparable or superior efficiency to standard GP-based BO on oxide and perovskite datasets.
  • Standard GP outperformed DKL when a strongly correlated descriptor for Curie temperature was directly usable.
  • DKL's capability for transfer learning was shown to further boost its efficiency.

Conclusions:

  • Bayesian optimization enhanced with deep kernel learning offers a more effective approach for exploring diverse material spaces compared to standard Gaussian processes.
  • DKL addresses the feature engineering limitations of traditional GP-based BO.
  • DKL-based BO holds significant promise for accelerating the discovery of novel materials.