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Deconvolution01:20

Deconvolution

162
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
162
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

450
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
450
Downsampling01:20

Downsampling

162
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
162
Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.5K

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Updated: Jul 9, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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材料発見のためのディープラーニングのスケーリング

Amil Merchant1, Simon Batzner2, Samuel S Schoenholz2

  • 1Google DeepMind, Mountain View, CA, USA. amilmerchant@google.com.

Nature
|November 29, 2023
PubMed
まとめ

グラフネットワークによる ディープラーニングは 非有機結晶の発見を 10倍に加速し 220万個の新しい安定した物質を 特定します この画期的な発見により 既知の安定材料の領域は 技術の応用により 大きく拡大しています

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科学分野:

  • 材料科学
  • コンピュータ化学
  • 人工知能

背景:

  • 伝統的な無機結晶の発見は 高価な試行錯誤方法に依存し 急速な技術的進歩を阻害しています
  • ディープラーニングモデルは,材料科学の応用の可能性を示唆し,様々な科学分野において重要な予測力を示しています.

研究 の 目的:

  • 非有機結晶の発見の効率と範囲を大幅に高めるために,大規模なグラフネットワークを開発し,適用する.
  • 人間の化学的直感を超えた新しい安定した結晶構造を特定し,既知の材料の風景を拡大する.

主な方法:

  • 4万8千個の既知の安定結晶のデータセットで グラフネットワークを訓練する.
  • 新しい安定した結晶構造を予測し,発見するためにスケール化されたディープラーニングを使用します.
  • 安定性と性質を検証するために 何億もの計算を行います

主要な成果:

  • 物質発見の効率を 大きく改善しました
  • 220万もの新しい安定した結晶構造が発見され,その多くは以前は知られていなかった.
  • 発見された安定した構造の736は実験的に検証された.
  • 分子ダイナミクスのシミュレーションのために高度に正確な学習された原子間潜在力を開発した.

結論:

  • 大規模なグラフネットワークは 材料発見のパラダイムシフトであり 伝統的な方法の限界を克服しています
  • 発見された材料は 清潔なエネルギーや 情報の処理などに 広大な可能性を秘めています
  • 開発されたコンピューティング・フレームワークと発見された材料は 科学的突破と技術革新を加速します