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相关概念视频

Exercise and Muscle Performance01:27

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Exercise induces a range of adaptations in muscle tissue, depending on the type and duration of activity. Such physical training can be broadly categorized into two types: endurance exercises and resistance exercises.
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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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动态训练增强了对长期分子动态的机器学习潜力.

Ivan Žugec1,2, Tin Hadži Veljković3, Maite Alducin1,4

  • 1Centro de Física de Materiales CFM/MPC, CSIC-UPV/EHU, Paseo Manuel de Lardizabal 5, Donostia-San Sebastián 20018, Spain.

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概括
此摘要是机器生成的。

动态训练 (DT) 提高了长分子动力学 (MD) 模拟的机器学习模型的准确性. 这种方法改善了复杂系统的预测,为计算物理和化学提供了实际的进步.

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科学领域:

  • 计算物理和化学 计算物理和化学
  • 材料科学是一种材料科学.
  • 机器学习是机器学习.

背景情况:

  • 分子动力学 (MD) 模拟对于研究复杂系统至关重要.
  • 机器学习 (ML) 潜力降低了计算成本,但在长时间的模拟中往往缺乏准确性.

研究的目的:

  • 引入动态训练 (DT),一种用于在扩展的MD模拟中提高ML模型准确性的新方法.
  • 为了证明DT在提高对具有挑战性的化学系统的预测准确性的有效性.

主要方法:

  • 开发并将动态训练 (DT) 应用于等价图神经网络 (EGNN).
  • 在一个与石墨烯上的团相互作用的分子上测试了DT增强的EGNN.

主要成果:

  • 与传统培训方法相比,DT显著提高了预测准确度.
  • DT方法在复杂的材料系统上表现出卓越的性能.

结论:

  • 动态训练 (DT) 是一种有效的,独立于架构的方法,用于在长时间的MD模拟中提高ML潜在的准确性.
  • DT为推进物理和化学计算模拟提供了一个实用的解决方案.