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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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コンピュータ駆動のワークフローと機械学習による高選択性の触媒の予測

Andrew F Zahrt1, Jeremy J Henle1, Brennan T Rose1

  • 1Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.

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まとめ

この研究は,非対称反応を加速するキラル触媒の選択のための計算アプローチを導入します. 機械学習モデルは触媒の選択性を正確に予測し 化学合成の効率を向上させます

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科学分野:

  • 非対称な触媒
  • コンピュータ化学
  • 化学における機械学習

背景:

  • 伝統的な触媒の設計は,経験的方法と質的パターン認識に依存しています.
  • 機械学習と化学情報学は,大規模なデータセットを分析することで,触媒の発見を加速する可能性を秘めています.
  • 非対称合成を進めるには,キラル触媒の選択性に関する予測モデルの開発が不可欠である.

研究 の 目的:

  • キラル触媒の選択のための計算によるワークフローを開発する.
  • 強力な分子記述器と普遍的なトレーニングセットのために化学情報学を利用する.
  • 触媒の選択性を正確に予測するための機械学習モデルを訓練する.

主な方法:

  • 化学情報工学を用いて 骨組みに無関係な分子記述子を作りました
  • ステリックと電子特性をベースにしたユニバーサルトレーニングセットを構築しました.
  • サポートベクトルマシンとディープ・フィード・フォワード・ニューラル・ネットワークを含む応用機械学習アルゴリズム.

主要な成果:

  • 幅広い範囲で触媒の選択性に関する非常に正確な予測モデルを達成した.
  • N-アシリミンへのキラルリン酸触媒のチオール添加で成功していることが実証されています.
  • 触媒の選択を導くための計算作業の有効性を検証した.

結論:

  • 開発された計算ワークフローは,キラル触媒の選択を大幅に加速します.
  • 機械学習モデルは,経験的方法の限界を克服して正確な予測を提供します.
  • このアプローチは,非対称的な反応開発の効率と範囲を高めます.