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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

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. Schrödinger...
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...

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相关实验视频

Updated: May 7, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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在Python中使用量子力学和机器学习方法的MLatom软件生态系统进行表面跳动动力学.

Lina Zhang1, Sebastian V Pios2, Mikołaj Martyka3

  • 1College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China.

Journal of chemical theory and computation
|June 5, 2024
PubMed
概括
此摘要是机器生成的。

我们介绍了MLatom@XACS,这是一个开源软件,用于使用机器学习和量子力学模拟非adiabatic动态. 它有效地复制分子异构化产量,使复杂化学反应的快速分析成为可能.

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科学领域:

  • 计算化学的计算化学
  • 化学物理 化学物理
  • 材料科学 材料科学 材料科学

背景情况:

  • 模拟非反应动力学对于理解光化学反应至关重要.
  • 现有的方法往往需要大量的计算资源.
  • 整合量子力学 (QM) 和机器学习 (ML) 为高效的模拟提供了一个有希望的途径.

研究的目的:

  • 介绍MLatom@XACS,一个开源软件生态系统,用于飞行表面跳跃非adiabatic动态.
  • 提供一个灵活的平台,结合各种QM和ML方法.
  • 为了证明AIQM1模型的效率和准确性.

主要方法:

  • 实施Landau-Zener-Belyaev-Lebedev算法进行表面跳跃.
  • 支持各种质量管理方法 (ab initio和半经验) 和ML潜力 (KREG,ANI,MACE).
  • 基于 Δ-学习的AIQM1模型的开发,以实现成本效益的动态.

主要成果:

  • 通过Python API,MLatom@XACS可以实现即时的非adiabatic动态模拟.
  • AIQM1模型以低计算成本准确地复制了跨-亚二的异构化量子产量.
  • 示例脚本有助于几何优化,初始条件采样和人口分析.

结论:

  • MLatom@XACS为非adiabatic动态提供了一个多功能和高效的工具.
  • 生态系统促进了化学动态的ML模型的开发和应用.
  • 未来与Newton-X的整合将扩大其用于先进的表面跳跃方法的能力.