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Hybridization of Atomic Orbitals I03:24

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The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
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sp3d and sp3d 2 Hybridization
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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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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).
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According to valence bond theory, a covalent bond results when: (1) an orbital on one atom overlaps an orbital on a second atom, and (2) the single electrons in each orbital combine to form an electron pair. The strength of a covalent bond depends on the extent of overlap of the orbitals involved. Maximum overlap is possible when the orbitals overlap on a direct line between the two nuclei.
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Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
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混合功能DFT计算的高效配置采样用于训练机器学习潜力:适用于大气化学.

Sungwoo Kang1,2, Runlong Cai3, Dong Sik Yang1

  • 1Air Science Research Center, Samsung Advanced Institute of Technology (SAIT), Samsung Electronics Co., LTD, 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16678, Republic of Korea.

Small methods
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概括
此摘要是机器生成的。

本研究介绍了一种主动转移学习方法,以高效地训练机器学习潜力 (MLP),使用高精度混合功能来进行分子动力学 (MD) 模拟. 这使得复杂化学系统的精确,大规模的原子模拟成为可能.

关键词:
大气化学 大气化学混合功能功能混合功能功能.机器学习的潜力分子动力学分子动力学其次是气溶二次形成.

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科学领域:

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

背景情况:

  • 机器学习潜力 (MLP) 对大规模分子动力学 (MD) 模拟至关重要.
  • 目前的MLP通常依赖于计算成本低廉,精度较低的方法.
  • 用高精度量子化学计算 (例如混合函数) 训练MLP在计算上是不可接受的.

研究的目的:

  • 开发一个高效的积极转移学习方案,用于为MLP生成高精度的训练数据集.
  • 为了使大规模的MD模拟使用MLP训练有素的混合功能.
  • 研究大气中的二次气溶形成和氧化反应.

主要方法:

  • 实施了积极转移学习策略,以智能选择昂贵的混合功能计算的配置.
  • 训练有素的MLP使用生成的数据集进行大气化学模拟.
  • 进行了纳秒级的MD模拟,以研究集群形成和氧化.

主要成果:

  • 开发的积极转移学习方案显著降低了生成混合功能培训数据的成本.
  • 经过这种方法培训的MLP可以达到与直接混合功能计算 (meV/原子内的误差) 相比的准确性.
  • 稳定的纳秒级MD模拟成功执行,导致纳米尺寸集群的形成.

结论:

  • 拟议的积极转移学习协议使高效和准确的高层次原子学模拟成为可能.
  • 这种方法有助于研究复杂的化学系统,例如大气气溶形成.
  • 为在各种化学领域进行高精度,大规模模拟的通用协议铺平了道路.