相关实验视频

Updated: Jun 17, 2025

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.4K

活跃的稀疏贝叶斯委员会机器潜力用于同热-同热分子动力学模拟

Soohaeng Yoo Willow1, Dong Geon Kim1, R Sundheep1

  • 1Department of Energy Science, Sungkyunkwan University, Seobu-ro 2066, Suwon 16419, Korea. cwmyung@skku.edu.

Physical chemistry chemical physics : PCCP
|August 8, 2024
PubMed
概括

基于内核的机器学习潜力 (MLP) 现在在分子动力学 (MD) 模拟中提供了准确的压力预测. 新的方法提高了各种材料模拟的计算效率.

更多相关视频

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches05:56

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Published on: October 13, 2022

1.3K
Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

2.2K

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
47