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Updated: Jun 23, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
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改进固体-液体接口的分子动力学模拟,使用机器学习的原子间潜力

Pengfei Hou1,2, Yumiao Tian1,2, Xing Meng1,2

  • 1Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), College of Physics, Jilin University, Changchun, 130012, China.

Chemistry (Weinheim an der Bergstrasse, Germany)
|June 14, 2024
PubMed
概括

人工智能正在通过机器学习原子间潜力 (MLIP) 彻底改变材料模拟. 这些MLIP提高了复杂系统分子动力学 (MD) 模拟的准确性和效率.

关键词:
原子间潜力是原子间的潜力.大型原子模型的大型原子模型机器学习是机器学习.分子动力学分子动力学固体液体接口 固体液体接口

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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...
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