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相关概念视频

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...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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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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Machines: Problem Solving I01:22

Machines: Problem Solving I

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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.
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...
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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...
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Associative Learning01:27

Associative Learning

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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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解决机器学习原子间潜力的身体顺序悖论.

Sanggyu Chong1, Tong Jiang2, Michelangelo Domina1

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

The Journal of chemical physics
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概括

机器学习的原子间潜力 (MLIP) 隐含地学习能量贡献. 这项研究揭示了MLIPs发展自己的身体秩序趋势,影响准确性和可概括性.

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科学领域:

  • 计算化学是一种计算化学.
  • 材料科学是一种材料科学.
  • 机器学习 机器学习

背景情况:

  • 机器学习的原子间潜力 (MLIPs) 经常将预测分解为体有序的能量贡献.
  • 固有的"有效的身体秩序"及其对MLIP准确性的影响仍然不太清楚.

研究的目的:

  • 为了研究MLIP如何将能量分解成身体排序的贡献.
  • 了解影响MLIPs有效身体秩序的因素.
  • 探索身体秩序对MLIP准确性和学习行为的影响.

主要方法:

  • 讨论了应用多体膨胀在初始计算中的挑战.
  • 在集群数据集上训练了各种MLIP.
  • 分析了新出现的身体秩序趋势和模型通用性.

主要成果:

  • MLIP表现出一种固有的倾向,即推断出有效的身体秩序趋势.
  • 这些趋势取决于ML模型类型和数据集组成.
  • 根据身体顺序趋势观察到不同的收和概括性.

结论:

  • MLIPs自行确定它们的体有序能量分解.
  • 了解这些新出现的趋势对于开发更准确和更可概括的MLIP至关重要.
  • 为未来的MLIP开发和应用提供了洞察力.