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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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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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Electronic Structure of Atoms

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An atom comprises protons and neutrons, which are contained inside the dense, central core called the nucleus, with electrons present around the nucleus. Taking into account the wave–particle duality of electrons and the uncertainty in position around the nucleus, quantum mechanics provides a more accurate model for the atomic structure. It describes atomic orbitals as the regions around the nucleus where electrons of discrete energy exist, characterized by four quantum...
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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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The Uncertainty Principle04:08

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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介绍用于原子模拟的机器学习潜力.

Fabian L Thiemann1,2, Niamh O'Neill2,3,4, Venkat Kapil3,4,5,6

  • 1IBM Research Europe, Daresbury, Warrington WA4 4AD, United Kingdom.

Journal of physics. Condensed matter : an Institute of Physics journal
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概括
此摘要是机器生成的。

机器学习潜力 (MLP) 正在改变原子模拟. 本指南介绍了开发MLP,涵盖描述符,模型和数据,以推进计算科学应用.

关键词:
原子学模拟的原子学模拟原子间相互作用 原子间相互作用机器学习潜力 机器学习潜力潜在能量的表面.

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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
  • 物理 物理学 物理

背景情况:

  • 机器学习潜力 (MLP) 显著提升了原子学模拟.
  • 许多MLP越来越多地成为计算科学家方法的组成部分.
  • 该领域需要可访问的指导来开发和应用这些强大的工具.

研究的目的:

  • 为机器学习潜力提供全面的概述和介绍.
  • 为开发MLP提供系统指南,包括关键组件和方法.
  • 展示MLP开发中的实际应用和最近的进展.

主要方法:

  • 对用于MLP开发的化学描述符和回归模型的审查.
  • 讨论数据生成和验证策略.
  • 探索历史模型 (例如,高维神经网络潜力,高斯近似潜力) 和最近的进展.

主要成果:

  • 创建和实施MLP的结构化指南.
  • 了解从早期模型到当前最先进的技术的演变.
  • 引用专家评价,开源软件和实际例子.

结论:

  • 机器学习潜力为原子模拟提供了强大的功能.
  • 这项工作降低了研究人员采用和利用MLP的障碍.
  • 许多MLP已经准备好在模拟中推动科学发现的边界.