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Thermodynamic Potentials

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...

Van der Waals Interactions

Van der Waals Interactions

Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.

Intermolecular vs Intramolecular Forces

Intermolecular vs Intramolecular Forces

Intermolecular forces (IMF) are electrostatic attractions arising from charge-charge interactions between molecules. The strength of the intermolecular force is influenced by the distance of separation between molecules. The forces significantly affect the interactions in solids and liquids, where the molecules are close together. In gases, IMFs become important only under high-pressure conditions (due to the proximity of gas molecules). Intermolecular forces dictate the physical properties of...

Propagation of Action Potentials

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...

Real Gases: Effects of Intermolecular Forces and Molecular Volume Deriving Van der Waals Equation

Real Gases: Effects of Intermolecular Forces and Molecular Volume Deriving Van der Waals Equation

Thus far, the ideal gas law, PV = nRT, has been applied to a variety of different types of problems, ranging from reaction stoichiometry and empirical and molecular formula problems to determining the density and molar mass of a gas. However, the behavior of a gas is often non-ideal, meaning that the observed relationships between its pressure, volume, and temperature are not accurately described by the gas laws.

Intermolecular Forces

Intermolecular Forces

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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Updated: Jan 7, 2026

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

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原子間ポテンシャルのための証拠深層学習

Han Xu^1,2, Taoyong Cui^1,3, Chenyu Tang¹

¹Shanghai Artificial Intelligence Laboratory, Shanghai, China.

Nature communications

|December 20, 2025

まとめ

この要約は機械生成です。

この研究は、機械学習原子間ポテンシャルのための証拠深層学習フレームワークを導入する。計算コストや精度の低下なしに、分子シミュレーションのための正確な不確かさ定量化を提供する。

キーワード:

証拠深層学習不確かさ定量化機械学習原子間ポテンシャル分子シミュレーション計算化学材料科学

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Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid

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

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Published on: September 17, 2021

科学分野:

計算化学
材料科学
機械学習

背景:

機械学習原子間ポテンシャル（MLIP）は、ab initio精度を提供する大規模分子シミュレーションにとって重要である。
能動学習は、不確かさを使用して、分布外のデータを特定することにより、トレーニングデータセットを反復的に拡張する。
MLIPの現在の不確かさ定量化（UQ）手法は、計算コストまたは予測精度のトレードオフに直面している。

研究の目的:

機械学習原子間ポテンシャル（MLIP）におけるUQのための新しい証拠深層学習フレームワークを開発する。
計算効率や予測精度を損なうことなく正確なUQを達成する。
分子シミュレーションにおけるUQのための堅牢で効率的な代替手段を提供する。

主な方法:

原子間ポテンシャルのための証拠深層学習フレームワークを提案する。
フレームワークは、物理学にインスパイアされた設計を組み込んでいる。
不確かさ定量化は、深層学習モデルに直接統合される。

主要な成果:

提案手法は、計算オーバーヘッドを最小限に抑えながら不確かさ定量化（UQ）を達成する。
予測精度は維持され、多様なデータセットにわたって既存のUQ手法を上回る。
水および普遍的ポテンシャルの原子配置を探索する応用が実証された。

結論:

証拠深層学習フレームワークは、MLIPのための計算効率的で正確なUQソリューションを提供する。
このアプローチは、大規模分子シミュレーションの信頼性を高める。
この手法は、分子シミュレーションと材料発見を進歩させる上で大きな可能性を示している。