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関連する概念動画

Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

4.0K
Crystallization is a phase transformation process in which crystals are precipitated from a supersaturated solution or formed from other sources. During crystallization, atoms or molecules arrange themselves into a well-defined, rigid crystal lattice to minimize energy.
Initiating crystallization involves manipulating the concentration of the solute and the temperature of the solution. Since crystal growth occurs when the ratio of concentration and solubility of the solute in the solvent...
4.0K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

42.9K
VSEPR Theory for Determination of Electron Pair Geometries
42.9K
Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

3.6K
Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
3.6K
Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

46.7K
Tetrahedral Complexes
Crystal field theory (CFT) is applicable to molecules in geometries other than octahedral. In octahedral complexes, the lobes of the dx2−y2 and dz2 orbitals point directly at the ligands. For tetrahedral complexes, the d orbitals remain in place, but with only four ligands located between the axes. None of the orbitals points directly at the tetrahedral ligands. However, the dx2−y2 and dz2 orbitals (along the Cartesian axes) overlap with the ligands less than the dxy,...
46.7K
Structures of Solids02:22

Structures of Solids

17.0K
Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
17.0K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

29.5K
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...
29.5K

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関連する実験動画

Updated: Dec 3, 2025

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
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部分的に監督学習を用いた結晶の構造ベースの合成可能性予測

Jidon Jang1, Geun Ho Gu1, Juhwan Noh1

  • 1Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Republic of Korea.

Journal of the American Chemical Society
|October 26, 2020
PubMed
まとめ

物質合成の予測は難しいものです 新しい機械学習モデルは,グラフコンボリューションニューラルネットワークを使用して,合成可能性 (CLscore) を正確に予測し,熱力学的な安定性だけでなく,材料の発見を改善します.

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Microcrystallography of Protein Crystals and In Cellulo Diffraction
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Microcrystallography of Protein Crystals and In Cellulo Diffraction

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Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography
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Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

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科学分野:

  • 材料科学
  • コンピュータ化学
  • 機械学習

背景:

  • 非有機物質の合成可能性を予測することは,加速された材料の発見に不可欠です.
  • 熱力学的分解の安定性は一般的なが限られた予測因子であり,多くの場合,候補物質が多すぎたり,メタステーブルな物質が欠けたりする.
  • 材料の合成可能性は熱力学的安定性以外の要因によって影響される複雑な現象である.

研究 の 目的:

  • 非有機物質合成の確率を定量化するための機械学習モデルを開発する.
  • 伝統的な熱力学的方法と比較して合成可能性の予測の精度を向上させる.
  • 合理的な材料設計のためのデータ主導のメトリックを提供し,実験的探査スペースを減らす.

主な方法:

  • ポジティブ・アンラベル (PU) 学習を用いた部分的に監督された学習アプローチを実装した.
  • グラフコンボリュショナルニューラルネットワークを,クリスタル類似度スコア (CLscore) を生成する分類器として利用した.
  • 最近の発見を含む実験的に報告された材料の大規模なデータベースでモデルを訓練し,検証しました.

主要な成果:

  • このモデルは,Materials Project の 9356 つの材料のテストセットで 87.4% の真の正の予測精度を達成しました.
  • 新しく報告された材料 (2015年−2019年) の検証では,86.2%の真の陽性率を示した.
  • CLscoreは,E_hullの能力を超えた合成可能な構造モチーフをキャプチャし,トップスコアの仮想材料の71%が以前に合成されています.

結論:

  • 開発された機械学習モデル (CLscore) は,素材の合成能力を効果的に定量化します.
  • このデータ駆動メトリックは,高通量仮想スクリーニングと材料発見のための生成モデルを大幅に強化します.
  • CLscoreは,実験的な検索スペースを縮小することで,より合理的な材料設計を容易にする.