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相关概念视频

The Bohr Model02:18

The Bohr Model

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Following the work of Ernest Rutherford and his colleagues in the early twentieth century, the picture of atoms consisting of tiny dense nuclei surrounded by lighter and even tinier electrons continually moving about the nucleus was well established. This picture was called the planetary model since it pictured the atom as a miniature “solar system” with the electrons orbiting the nucleus like planets orbiting the sun. The simplest atom is hydrogen, consisting of a single proton as...
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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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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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Deactivation Processes: Jablonski Diagram01:25

Deactivation Processes: Jablonski Diagram

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Luminescence, the emission of light by a substance that has absorbed energy, is a process that involves the interaction of molecules with light. The energy-level diagram, or Jablonski diagram, is a graphical representation of these interactions, illustrating the various states and transitions a molecule can undergo. In a typical Jablonski diagram, the lowest horizontal line represents the ground-state energy of the molecule, which is usually a singlet state. This state represents the energies...
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Atomic Nuclei: Nuclear Spin State Overview01:03

Atomic Nuclei: Nuclear Spin State Overview

938
NMR-active nuclei have energy levels called 'spin states' that are associated with the orientations of their nuclear magnetic moments. In the absence of a magnetic field, the nuclear magnetic moments are randomly oriented, and the spin states are degenerate. When an external magnetic field is applied, the spin states have only 2 + 1 orientations available to them. A proton with = ½ has two available orientations. Similarly, for a quadrupolar nucleus with a nuclear spin value of...
938
Atomic Nuclei: Nuclear Relaxation Processes01:23

Atomic Nuclei: Nuclear Relaxation Processes

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In the absence of an external magnetic field, nuclear spin states are degenerate and randomly oriented. When a magnetic field is applied, the spins begin to precess and orient themselves along (lower energy) or against (higher energy) the direction of the field. At equilibrium, a slight excess population of spins exists in the lower energy state. Because the direction of the magnetic field is fixed as the z-axis,  the precessing magnetic moments are randomly oriented around the z-axis.
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Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
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从物理限制的机器学习中获得电子兴奋状态.

Edoardo Cignoni1, Divya Suman2, Jigyasa Nigam2

  • 1Dipartimento di Chimica e Chimica Industriale, Università di Pisa, 56126 Pisa, Italy.

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概括

我们开发了一种混合方法,将机器学习 (ML) 与物理原理相结合,以实现更准确,更高效的材料建模. 这种方法提高了ML模型在电子结构计算中的可转移性和可解释性.

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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:

  • 数据驱动的方法越来越多地取代了传统的电子结构计算.
  • 一个关键的问题是是否使用纯粹的机器学习 (ML) 或将其与物理原理集成.

研究的目的:

  • 探索一种综合建模方法,将机器学习与物理结合起来,用于电子结构计算.
  • 评估数据驱动技术与物理近似交织的好处.

主要方法:

  • 开发了一个有效的哈密尔顿式的对称性适应的ML模型.
  • 训练了ML模型来复制量子力学计算中的电子激发.
  • 使用了对应于最小原子中心基础的参数化.

主要成果:

  • 综合模型准确地预测了比训练中使用的更大,更复杂的分子的特性.
  • 通过间接定位计算输出,实现了显著的计算节省.
  • 证明了ML模型的可转移性和可解释性,而不会影响准确性或效率.

结论:

  • 将数据驱动的ML与物理近似相结合,为材料建模提供了一个强大的方法.
  • 这一综合战略为开发先进的ML增强电子结构方法提供了蓝图.
  • 这种方法提高了ML在计算科学中的可靠性和适用性.