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相关概念视频

The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

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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 hydrogen spectra.
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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Molecular Models02:00

Molecular Models

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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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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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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
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相关实验视频

Updated: Jun 9, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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构建准确和高效的通用原子化机器学习模型,其准确性可用于量子化学.

Yicheng Chen1, Wenjie Yan1, Zhanfeng Wang1

  • 1Department of Chemistry, Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Fudan University, Shanghai 200433, People's Republic of China.

Journal of chemical theory and computation
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概括
此摘要是机器生成的。

原子式机器学习 (ML) 模型现在为大规模模拟提供了相当于DFT的准确性. 新的XPaiNN模型,特别是与Δ-ML,实现了高性能和可用于各种化学系统的可转移性.

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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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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
  • 量子力学就是量子力学.

背景情况:

  • 密度函数理论 (DFT) 对于通过量子力学 (QM) 计算来理解分子和材料特性至关重要.
  • 原子式机器学习 (ML) 为大规模模拟提供了更便宜的计算替代方案,实现了DFT级准确性.
  • 开发通用ML模型面临能力,数据效率和跨化学系统的可转移性方面的挑战.

研究的目的:

  • 介绍XPaiNN,它是可两极分化的原子相互作用神经网络的新型扩展.
  • 解决通用原子化ML模型开发的挑战,重点关注容量,数据效率和可转移性.
  • 在同一框架内比较直接学习和 Δ-ML 培训策略.

主要方法:

  • 开发了XPaiNN,这是可极化原子相互作用神经网络的延伸.
  • 采用了两种培训策略:直接学习和 Δ-ML 基于半实证的 QM 方法.
  • 在直接比较的统一框架内实施了这两种方法.

主要成果:

  • XPaiNN 模型,特别是 Δ-ML 变体,在基准指标上表现出竞争力.
  • 在各种下游任务上与其他ML模型和QM方法相比,证明了有效性.
  • 在非共价相互作用,反应能量,屏障高度和几何优化方面的验证性能.

结论:

  • XPaiNN在创建准确和高效的通用原子 ML 模型方面取得了重大进展.
  • Δ-ML 方法对处理复杂的化学系统具有可转移的准确性特别有希望.
  • 这项工作为ML在计算化学和材料科学中的更广泛应用铺平了道路.