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相关概念视频

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
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Thermodynamic Potentials01:26

Thermodynamic Potentials

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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相关实验视频

Updated: Jun 18, 2026

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

为通用机器学习优化跨领域转移原子间潜能.

Jaesun Kim1, Jinmu You1, Yutack Park1

  • 1Department of Materials Science and Engineering, Seoul National University, Seoul, Republic of Korea.

Nature communications
|March 3, 2026
PubMed
概括
此摘要是机器生成的。

我们开发了一种新的机器学习原子间潜力的培训策略,提高模型的准确性和跨不同化学领域的可转移性. 这种方法通过使分子,晶体和表面的可靠预测,加速了材料的发现.

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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科学领域:

  • 计算材料科学 计算材料科学
  • 机器学习在化学中的应用
  • 量子力学就是量子力学.

背景情况:

  • 准确和可转移的机器学习原子间潜力 (MLIP) 对于加速材料和化学发现至关重要.
  • 现有的通用MLIP经常因过度适应特定的化学空间或计算方法而受到影响,这限制了它们在各种应用中的可靠性.
  • 这就需要开发出能够在不同的化学环境和功能领域进行概括的强大模型.

研究的目的:

  • 引入一种新的可转移的多领域培训策略,以开发准确和可通用的MLIP.
  • 增强MLIP的分布外通用化能力,同时保持高域内精度.
  • 创建一个通用的MLIP模型,SevenNet-Omni,能够跨越多种化学领域和量子力学忠实度.

主要方法:

  • 实施了多领域培训策略,通过选择性规范化优化参数.
  • 利用域桥接数据集,在不同的化学环境中对准潜在能量表面.
  • 进行了系统的废弃实验,以验证拟议策略的协同效应.
  • 在15个不同的开放数据集上训练了SevenNet-Omni模型,包括分子,晶体和表面.

主要成果:

  • 通过开发的策略,证明了在分发之外的泛化和域内忠实性的协同增强.
  • 在跨领域基准中实现了最先进的准确性,在各种场景中达到化学准确性.
  • 通过从更大,更低准确度的数据库转移知识,成功地复制了高保真性质.
  • 在预测催化表面和金属有机框架的吸附能量方面,SevenNet-Omni表现出色.

结论:

  • 拟议的可转移的多领域培训策略显著提高了MLIP的可靠性和适用性.
  • 七网-万能代表着对材料和化学发现的通用,可转移的原子间潜力的重大进步.
  • 这个框架为开发模型提供了一个可扩展的途径,可以将量子力学准确性和广泛的化学领域覆盖度相结合.