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Intermolecular forces are attractive forces that exist between molecules. They dictate several bulk properties, such as melting points, boiling points, and solubilities (miscibilities) of substances. Molar mass, molecular shape, and polarity affect the strength of different intermolecular forces, which influence the magnitude of physical properties across a family of molecules.
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Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
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Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
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Atoms and molecules interact through bonds (or forces): intramolecular and intermolecular. The forces are electrostatic as they arise from interactions (attractive or repulsive) between charged species (permanent, partial, or temporary charges) and exist with varying strengths between ions, polar, nonpolar, and neutral molecules. The different types of intermolecular forces are ion–dipole, dipole–dipole, hydrogen bonds, and dispersion; among these, dipole–dipole, hydrogen...
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离子液体模拟与等价机器学习原子间潜力的可转移性和准确性

Zachary A H Goodwin1, Malia B Wenny2, Julia H Yang1,3

  • 1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States.

The journal of physical chemistry letters
|July 18, 2024
PubMed
概括

机器学习的原子间潜力 (MLIP) 可以准确模拟离子液体 (IL),甚至是新的混合物. 这项研究表明,MLIP在组成上可以用于各种IL电解质应用.

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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
  • 电化学 电化学 电化学

背景情况:

  • 离子液体 (ILs) 是具有可调节性质的多功能电解质,在储能及其他领域充当"设计溶剂".
  • 机器学习原子间潜能 (MLIP) 的应用用于模拟IL仍然是一个未被充分探索的领域.

研究的目的:

  • 评估MLIPs用于模拟离子液体的可行性和变革潜力.
  • 为了确定MLIP是否可以在IL混合物和添加剂的组成上进行转移.

主要方法:

  • 在密度函数理论 (DFT) 对离子液体计算的有限数据集上训练MLIP.
  • 评估MLIP在预测IL混合物不包括在训练套件中的特性方面的准确性.
  • 合成和实验性表征一种新的离子液体.

主要成果:

  • 证明了MLIP的组成可转移性,使IL混合物的准确模拟成为可能.
  • 开发的MLIP显示了与实验数据和DFT计算对新型IL的合理一致.
  • 在大约200个DFT上训练的MLIPs被证明是有效的.

结论:

  • MLIP对推进离子液体的模拟和设计具有显著的前景.
  • 组合转移性是MLIPs处理复杂IL电解质系统的关键特征.
  • MLIP为研究新型IL提供了一种计算效率高,准确的方法.