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相关概念视频

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

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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.
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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing...
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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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快速灵活的远程模型用于原子化机器学习.

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科学领域:

  • 计算化学计算化学
  • 材料科学 材料科学 材料科学
  • 机器学习 机器学习

背景情况:

  • 原子式机器学习 (ML) 模型通常使用局部近似,限制它们模拟远程相互作用的能力,如静电学.
  • 在ML中解决远程影响的现有方法通常是低效的,需要临时实施.

研究的目的:

  • 开发一个统一的框架,将已建立的远程交互算法集成到原子 ML 中.
  • 提供高效和模块化实现,用于在ML模型中评估非绑定相互作用.
  • 引入适合 ML 应用的新型描述符,以远程物理为主.

主要方法:

  • 将Ewald总和,经典粒子网Ewald (PME) 和粒子-粒子/粒子网 (PPPM) 纳入原子学ML框架.
  • 在PyTorch (torch-pme) 中开发参考实现和在JAX (jax-pme) 中进行实验.
  • 引入了聚焦非局部原子环境的纯化描述符.

主要成果:

  • 快速,功能丰富和模块化实现,用于准确评估物理远程力量.
  • 远程模型与使用自动区分的本地ML方案的无组合.
  • 在分子动力学模拟,ML潜力训练和长距离等同变量描述符的评估中证明有用.

结论:

  • 开发的框架和库有效地解决了原子 ML 中局部近似的局限性.
  • 能够构建精确的 (半) 实证基线潜力和复杂的ML架构,包括物理相互作用.
  • 促进先进的分子模拟和新型ML模型的开发,用于具有显著远程效应的系统.