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関連する概念動画

Thermodynamic Potentials01:26

Thermodynamic Potentials

1.5K
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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Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

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Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
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Valence Bond Theory and Hybridized Orbitals02:38

Valence Bond Theory and Hybridized Orbitals

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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.
A σ bond (single bond in a Lewis structure) is a covalent bond in which the electron density is...
27.9K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Interfacial Electrochemical Methods: Overview01:06

Interfacial Electrochemical Methods: Overview

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Interfacial electrochemical methods focus on the phenomena occurring at the boundary between an electrode and a solution, as opposed to bulk methods that concentrate on the solution's overall properties. These interfacial methods are classified as either static or dynamic based on the presence of a nonzero current in the electrochemical cell and the consistency of analyte concentrations. Static methods, such as potentiometry, measure the cell's potential without any significant current...
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Lewis Structures of Molecular Compounds and Polyatomic Ions02:54

Lewis Structures of Molecular Compounds and Polyatomic Ions

44.8K
To draw Lewis structures for complicated molecules and molecular ions, it is helpful to follow a step-by-step procedure as outlined:
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Updated: Jan 18, 2026

Influence of Hybrid Perovskite Fabrication Methods on Film Formation, Electronic Structure, and Solar Cell Performance
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Influence of Hybrid Perovskite Fabrication Methods on Film Formation, Electronic Structure, and Solar Cell Performance

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ペロブスカイト構造最適化のための統一グラフベース原子間ポテンシャル

Maitreyo Biswas1, Rushik Desai1, Gavin Bidna1

  • 1School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, United States.

Journal of chemical information and modeling
|January 16, 2026
PubMed
まとめ
この要約は機械生成です。

ハロゲン化ペロブスカイト（HaPs）の特性を予測する機械学習モデルを開発しました。この統一アプローチは、新しい材料発見のために複雑な構造を効率的に探索します。

キーワード:
ハロゲン化ペロブスカイト機械学習原子間ポテンシャル構造最適化材料発見

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

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

  • 材料科学
  • 計算化学
  • 機械学習

背景:

  • ハロゲン化ペロブスカイト（HaPs）は、オプトエレクトロニクスと触媒の分野で有望視されています。
  • その複雑な組成空間（合金、欠陥、表面）は最適化を妨げます。
  • HaPポテンシャルエネルギー面の効率的な探索は困難です。

研究 の 目的:

  • HaPsのための統一されたグラフベースの深層学習原子間ポテンシャルを開発すること。
  • 多様なHaP構造にわたるエネルギーの効率的な最適化と予測を可能にすること。
  • HaPsの複雑なポテンシャルエネルギー面（PES）をナビゲートすること。

主な方法:

  • 約12,000のHaP構造の包括的なDFTデータセットでトレーニングされたM3GNetベースの機械学習原子間ポテンシャル（IAP）。
  • トレーニングデータには、バルク合金、ネイティブ/不純物欠陥、表面スラブが含まれていました。
  • IAPフレームワークは、勾配ベースの最適化のためにエネルギー、力、応力でトレーニングされました。

主要な成果:

  • M3GNet-IAPは、複雑なHaP PES全体で堅牢な一般化能力を示しました。
  • 低い予測誤差を達成しました：エネルギー（3.7 meV/atom）、力（16.5 meV/Å）、応力（5.5 MPa）。
  • HaPsの形成、分解、欠陥、表面エネルギーを正確に予測しました。

結論:

  • 統一された代理モデルは、HaP幾何最適化への全体的なアプローチを提供します。
  • この方法は、HaPsにおける多様な構造的変動の効率的な探索を容易にします。
  • このモデルは、新しいHaP組成、欠陥、ドーパント、表面特性の発見に革命的です。