Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Thermodynamic Potentials01:26

Thermodynamic Potentials

972
Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
972
Thermodynamic Systems01:06

Thermodynamic Systems

5.4K
A thermodynamic system is a set of objects whose thermodynamic properties are of interest. The system is considered to be embedded in its surroundings or the environment. The system and its environment can exchange heat and do work on each other through a boundary that separates them. However, the immediate surroundings of the system interact with it directly and therefore have a much stronger influence on its behavior and properties.
Consider an example of  tea boiling in a kettle. The...
5.4K
First Law Of Thermodynamics: Problem-Solving01:21

First Law Of Thermodynamics: Problem-Solving

2.9K
The first law of thermodynamics states that the change in internal energy of the system is equal to the net heat transfer into the system minus the net work done by the system. This equation is a generalized form of energy conservation and can be applied to any thermodynamic process.
The following strategies can be used to solve any problem involving the first law of thermodynamics.
2.9K
Heat Capacity: Problem-Solving01:17

Heat Capacity: Problem-Solving

609
The heat capacity of a gas is the amount of heat energy required to raise the temperature of a unit mass of gas by one degree Celsius. It is an important thermodynamic property of gases, and its determination is essential in many industrial and scientific applications. Here are the steps to solve problems related to the heat capacities of gases:
Determine the type of gas: The heat capacity of a gas depends on its molecular structure and the degree of freedom of its molecules. Different types of...
609
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.7K
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
1.7K
Thermosensation01:43

Thermosensation

31.8K
Peripheral thermosensation is the perception of external temperature. A change in temperature (on the surface of the skin and other tissues) is detected by a family of temperature-sensitive ion channels called Transient Receptor Potential, or TRP, receptors. These receptors are located on free nerve endings. Those detecting cold temperatures are closer to the surface of the skin than the nerve endings detecting warmth. These thermoTRP channels, while temperature selective, have relatively...
31.8K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Band Gap Prediction of Two-Dimensional Materials Using a Gradient-Boosted Feature Selection Approach.

Journal of chemical information and modeling·2026
Same author

Machine-Learning Predictions of Photoluminescence in Molecules Exhibiting Thermally Activated Delayed Fluorescence with Implicit Experimental Validation.

Journal of chemical information and modeling·2026
Same author

Automatic Generation of a Mechanical Properties Question-Answering Data Set for Language Model Benchmarking: A Comparative Study of BERT, XLNet, and LLaMA Models.

Journal of chemical information and modeling·2026
Same author

A dataset of Curie and Néel temperatures auto-generated with ChemDataExtractor and the Snowball algorithm.

Scientific data·2025
Same author

Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks.

Journal of chemical information and modeling·2025
Same author

Cost-Efficient Domain-Adaptive Pretraining of Language Models for Optoelectronics Applications.

Journal of chemical information and modeling·2025

相关实验视频

Updated: Sep 12, 2025

Asymmetric Thermoelectrochemical Cell for Harvesting Low-grade Heat under Isothermal Operation
09:09

Asymmetric Thermoelectrochemical Cell for Harvesting Low-grade Heat under Isothermal Operation

Published on: February 5, 2020

7.0K

自动生成来自热电材料数据库的域特定问题答案数据集,以实现高性能BERT模型.

Odysseas Sierepeklis1, Jacqueline M Cole1,2

  • 1Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.

Journal of chemical information and modeling
|August 7, 2025
PubMed
概括

我们开发了一种方法,可以自动创建热电材料的大型问答 (QA) 数据集. 在这个特定领域的数据上微调BERT模型可以显著提高其在材料科学应用中的性能.

更多相关视频

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
10:23

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

Published on: December 1, 2023

542
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.2K

相关实验视频

Last Updated: Sep 12, 2025

Asymmetric Thermoelectrochemical Cell for Harvesting Low-grade Heat under Isothermal Operation
09:09

Asymmetric Thermoelectrochemical Cell for Harvesting Low-grade Heat under Isothermal Operation

Published on: February 5, 2020

7.0K
Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
10:23

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

Published on: December 1, 2023

542
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.2K

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算语言学 计算语言学
  • 人工智能的人工智能

背景情况:

  • 对特定领域的数据集对于训练高性能语言模型至关重要.
  • 现有的通用QA数据集可能无法捕捉热电材料等专业科学领域的细微差别.
  • 小语言模型 (SLM) 提供了计算效率,但通常需要量身定制的训练数据.

研究的目的:

  • 介绍一种用于自动生成热电材料的大型,特定领域的问题和答案 (QA) 数据集的方法.
  • 与通用数据集相比,在此数据集上评估微调的BERT模型的性能.
  • 调查混合特定领域和通用质量保证数据对模型性能的影响.

主要方法:

  • 从热电材料数据库中自动生成99,757个QA对数据集.
  • 在自动生成数据集,通用SQuAD-v2数据集和混合数据集上微调BERT语言模型.
  • 在专门的测试集上使用精确匹配和F1分数对模型性能进行评估.

主要成果:

  • 在自动生成的域特定数据集上微调的BERT模型优于在SQuAD-v2.2上训练的模型.
  • 混合域特定和通用数据集导致了最佳性能.
  • 最好的模型在测试数据上获得了67.93%的精确匹配得分和72.29%的F1得分.

结论:

  • 自动生成的,域特定的QA数据集可以显著提高专业领域的小语言模型的性能.
  • 将特定领域的数据与通用数据集相结合,为语言模型培训提供了协同效益.
  • 这种方法可以开发高性能的SLMs,用于材料科学应用的适度计算资源.