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相关概念视频

Potential Energy00:52

Potential Energy

The energy stored by a structure and location of matter in space is called potential energy. For instance, raising a kettlebell changes its spatial location and increases its potential energy. Similarly, a stretched rubber band contains potential energy which, under certain conditions, can be converted into other forms of energy, such as kinetic energy.
Chemical bonds that form attractive forces between atoms also contain potential energy, called chemical energy. When a chemical reaction...
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

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...
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

sp3d and sp3d 2 Hybridization
Valence Bond Theory and Hybridized Orbitals02:38

Valence Bond Theory and Hybridized Orbitals

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.
A σ bond (single bond in a Lewis structure) is a covalent bond in which the electron density is...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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相关实验视频

Updated: Jun 16, 2026

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

在PES-Learn中的方法:波恩-奥本海默潜在能量表面的直接适合机器学习.

Ian T Beck1, Justin M Turney1, Henry F Schaefer1

  • 1Department of Chemistry, Center for Computational Quantum Chemistry, University of Georgia, Athens, GA 30602, USA.

Molecules (Basel, Switzerland)
|January 10, 2026
PubMed
概括
此摘要是机器生成的。

PES-Learn 1.0是一个新的开源软件,用于构建分子潜在能量表面 (PES) 的机器学习模型. 它引入了内核回归和增强的Python API,以更轻松地构建PES和梯度预测.

关键词:
核心脊回归的回归方法机器学习是机器学习.神经网络的神经网络的神经网络潜在的表面能量 表面能量

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Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Last Updated: Jun 16, 2026

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
13:56

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

科学领域:

  • 计算化学是一种计算化学.
  • 机器学习 机器学习
  • 材料科学是一种材料科学.

背景情况:

  • 开发精确的分子潜在能量表面 (PES) 对于模拟化学过程至关重要.
  • 传统的PES构建方法在计算上可能昂贵且耗时.
  • 使用机器学习自动化构建 PES 提供了一个有前途的替代方案.

研究的目的:

  • 介绍PES-Learn 1.0版本,这是一个开源软件包,用于自动构建半全球分子潜在能量表面 (PES).
  • 在PES-Learn中引入和评估内核回归 (KRR) 作为一种新的机器学习方法.
  • 增强PES-Learn的互操作性,并通过基准测试来证明其性能.

主要方法:

  • 发布PES-Learn版本1.0与一个新的Python API,以改善与QCS的互操作性.
  • 实施和评估用于PES构建的核心回归 (KRR).
  • 在PES-Learn中使用rMD17数据库中的和乙醇数据集对所有可用的机器学习方法进行比较.
  • 使用神经网络模型评估配套性能,定时和梯度预测能力.

主要成果:

  • PES-Learn 1.0促进了半全球 PES 的自动构建,并增强了互操作性.
  • 核心回归 (KRR) 证明了对选择半全球 PESs 建模的有效能力.
  • 基准测试显示了PES-Learn在和乙醇的适配精度和计算效率方面的表现.
  • 神经网络模型成功地预测了乙醇和的梯度.

结论:

  • PES-Learn 1.0为基于机器学习的 PES 构建提供了一个强大的,用户友好的平台.
  • 纳入KRR扩大了PES建模的方法选择.
  • 该软件表现出强大的性能和在计算化学研究中更广泛采用的潜力.
  • PES-Learn是一个积极的项目,鼓励社区为未来的发展做出贡献.