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関連する概念動画

Machines01:19

Machines

581
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...
581
Machines: Problem Solving II01:30

Machines: Problem Solving II

678
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.
678
Machines: Problem Solving I01:22

Machines: Problem Solving I

727
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.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
727
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Potential Energy00:52

Potential Energy

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The energy stored by a structure and location of matter in space is called potential energy. For instance, raising a kettlebell changes its spatial location and increases its potential energy. Similarly, a stretched rubber band contains potential energy which, under certain conditions, can be converted into other forms of energy, such as kinetic energy.
Chemical bonds that form attractive forces between atoms also contain potential energy, called chemical energy. When a chemical reaction...
42.9K
Associative Learning01:27

Associative Learning

1.5K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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機械学習の体順序パラドックスを解く 原子間ポテンシャル

Sanggyu Chong1, Tong Jiang2, Michelangelo Domina1

  • 1Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.

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まとめ

機械学習の原子間潜在力 (MLIP) は,暗黙的にエネルギー貢献を学習する. この研究は,MLIPが独自の身体秩序の傾向を発展させ,正確性と一般化性に影響を与えることを明らかにしています.

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科学分野:

  • 計算化学はコンピュータ化学である.
  • マテリアルサイエンス 材料科学
  • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) というものです.

背景:

  • 機械学習の原子間ポテンシャル (MLIPs) は,しばしば予測を体によるエネルギー貢献に分解する.
  • 固有の"有効な身体秩序"とそのMLIPの精度への影響は,まだ十分に理解されていません.

研究 の 目的:

  • MLIPがどのようにエネルギーを身体に割り当てられた貢献に分解するのかを調査する.
  • MLIPの有効な身体秩序を左右する要因を理解する.
  • MLIPの精度と学習行動に身体の秩序が与える影響を調査する.

主な方法:

  • Ab initio計算に多体膨張を適用する際の課題について議論しました.
  • 水素クラスターデータセットに関する様々なMLIPのトレーニングを行いました.
  • 発生するボディ・オーダー・トレンドとモデルの一般化性を分析した.

主要な成果:

  • MLIPは,実際のボディ・オーダー・トレンドを推論する固有の傾向を示しています.
  • これらの傾向は,MLモデルのタイプとデータセットの構成に依存しています.
  • ボディ・オーダー・トレンドに基づいて,変化する収束と一般化が見られた.

結論:

  • MLIPは,身体に秩序付けられたエネルギー分解を自己決定する.
  • これらの新興傾向を理解することは,より正確で一般化可能なMLIPの開発に不可欠です.
  • 将来のMLIPの開発とアプリケーションに関する洞察を提供します.