相关实验视频

Updated: Jan 20, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

揭示硬碳中的储存机制,通过机器学习驱动的模拟,与准确的现场占用识别集成

Zhaoming Wang^1,2,3, Guanghui Shi^1,2,3, Guanghui Wang^2,4

¹Suzhou Laboratory Suzhou 215000 China.

Chemical science

|January 19, 2026

概括

机器学习模拟揭示了离子电池硬碳阳极中的储存机制. 这种方法澄清了电压配置文件,并确定了潜在的安全风险,例如过度充电时形成集群.

科学领域:

材料科学材料科学材料科学
电化学电化学电化学
计算化学计算化学

背景情况:

硬碳 (HC) 是离子电池 (SIB) 的一个有前途的阳极,因为它的容量,稳定性和成本.
由于其复杂的结构和相互作用,了解HC中的储存机制具有挑战性.

研究的目的:

用机器学习研究HC阳极中的储存行为.
阐明HC中插入和电压配置文件的复杂机制.

主要方法:

利用机器学习潜力 (MLP) 与随机森林框架集成,用于识别插入地点.
模拟的连续电压配置文件,以逐步插入,包括过充电状态.

主要成果:

该MLP准确地捕获了HC结构和插入行为.
模拟的电压配置文件重现了实验观察结果,区分吸附,间接和孔隙填充.
鉴定了由于短的Na-Na距离而导致过载的排斥和负电压,并观察到集群的形成,表明安全风险.

结论:

基于机器学习的模拟为HC阳极中的储存机制提供了强大的洞察力.
这种方法有助于理解和优化SIB的HC阳极.
该方法可扩展到其他电池组件和系统.

更多相关视频

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

相关概念视频

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

We describe a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and a machine learning algorithm. Measurements of 3D refractive index tomograms of lymphocytes present 3D morphological and biochemical information for individual cells, which is then analyzed with a machine-learning algorithm for identification of cell...

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

This article explains how to use simulation-supervised machine learning for analyzing mitochondria morphology in fluorescence microscopy images of fixed cells.

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and...

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to...

A Virtual Simulation Experiment of Mechanics: Material Deformation and Failure Based on Scanning Electron Microscopy

A Virtual Simulation Experiment of Mechanics: Material Deformation and Failure Based on Scanning Electron Microscopy

This work presents a three-dimensional virtual simulation experiment for material deformation and failure that provides visualized experimental processes. Through a set of experiments, users can become familiar with the equipment and learn the operations in an immersive and interactive learning...