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関連する概念動画

Range00:59

Range

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The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
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Physical and Chemical Properties of Matter02:57

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The characteristics that enable us to distinguish one substance from another are called properties.
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¹H NMR: Long-Range Coupling01:27

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The coupling interactions of nuclei across four or more bonds are usually weak, with J values less than 1 Hz. While these are usually not observed in spectra, the presence of multiple bonds along the coupling pathway can result in observable long-range coupling.
In alkenes, spin information is communicated via σ–π overlap, as seen in allylic (four-bond) and homoallylic (five-bond) couplings. These coupling interactions are stronger when the σ bond is parallel to the alkene...
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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Physics is concerned with the interactions of energy, matter, space, and time, in order to discover the underlying mechanisms that underpin all phenomena. The word "physics" comes from the Greek word "phúsis", which means nature. Physics seeks to comprehend the natural world around us at its most fundamental level. It emphasizes the use of quantitative laws to do this, which could be valuable in other fields that want to push the performance boundaries of present...
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Problem-solving is the ability to apply general physical principles to specific situations, usually expressed by equations. It is an essential skill in physics, and can also be useful for applying physics in everyday life as well. Analytical skills and problem-solving abilities can be applied to new situations, compared to a list of facts, which can never be extensive enough to include every possible circumstance. To solve physics problems, a certain amount of creativity and insight is...
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深層学習に基づく物理情報付き長距離分極ポテンシャル

Z Li1, S Scandolo1

  • 1The Abdus Salam International Centre for Theoretical Physics, Trieste 34151, Italy.

The Journal of chemical physics
|January 23, 2026
PubMed
まとめ
この要約は機械生成です。

本研究では、分極性材料における長距離静電相互作用を正確にモデル化する物理情報付き機械学習ポテンシャルを導入する。この新しい手法は、重要な分極効果を捉えることにより、水やペロブスカイトのような系のシミュレーションを改善する。

キーワード:
深層学習物理情報付き機械学習分極性材料長距離静電相互作用原子シミュレーション

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科学分野:

  • 計算材料科学
  • 物性物理学
  • 化学における人工知能

背景:

  • 機械学習原子間ポテンシャルは、原子シミュレーションにとって重要である。
  • 既存のモデルは、分極性および生体分子系における長距離静電相関に苦労している。

研究 の 目的:

  • 長距離静電相互作用を正確に捉える物理情報付き機械学習原子間ポテンシャルを開発すること。
  • 原子シミュレーションを用いた分極性および生体分子系のモデリングを強化すること。

主な方法:

  • 短距離および双極子相互作用のために、2つの等変メッセージパッシングニューラルネットワークを組み合わせた。
  • 長距離静電相互作用をモデル化するために、分極性フレームワークを組み込んだ。
  • エネルギー、力、およびボーン実効電荷テンソルでモデルを訓練した。

主要な成果:

  • イオン結晶、液体水、ハロゲン化ペロブスカイトにおける長距離分極効果のモデリングの改善を実証した。
  • エネルギーと力の予測において、競争力のある精度を達成した。
  • 赤外吸収スペクトルなどの場誘起特性の正確な予測を可能にした。

結論:

  • 絶縁性および分極性材料の正確なシミュレーションには、長距離静電相互作用の明示的なモデリングが不可欠である。
  • 開発されたポテンシャルは、強い分極効果を持つ系の原子シミュレーションに大きな進歩を提供する。
  • 計算材料科学、物性物理学、化学における人工知能