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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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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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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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概括
此摘要是机器生成的。

我们使用人工智能硬件开发了用于量子化学模拟的新算法,大大加快了像FeMo辅因子这样复杂分子的计算速度. 这一突破使得实现精确的解决方案比以往任何时候都更快.

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科学领域:

  • 量子化学是一种量子化学.
  • 计算材料科学 计算材料科学
  • 科学计算中的人工智能

背景情况:

  • 准确的量子化学计算对于理解分子行为和设计新材料至关重要.
  • 传统的计算方法面临着复杂系统的缩放局限性,阻碍了催化和材料发现等领域的进展.
  • 需要混合CPU-multiGPU方法结合先进的算法来克服这些计算障碍.

研究的目的:

  • 为混合CPU-multiGPU张量网络状态算法引入新的算法解决方案.
  • 为了提高计算性能,利用非阿贝尔对称性和人工智能驱动的硬件/软件技术.
  • 证明这些新方法对化学相关系统的大规模模拟的能力.

主要方法:

  • 开发混合CPU-multiGPU张量网络算法,结合非阿贝尔对称性.
  • 大规模的SU(2) 旋转适应密度矩阵重规范化组 (DMRG) 计算.
  • 在NVIDIA A100设备上利用NVIDIA Tensor Cores进行高性能计算.

主要成果:

  • 数字模拟在传统方法所需的一小部分时间内实现了完整的活性空间 (CAS) 计算的完整配置交互 (完整CI) 极限,最高可达CAS ((18,18).
  • 高达CAS ((113, 76) 的基准测试表明,在一个节点上,使用八个NVIDIA A100设备的性能约为115 TFLOPS,达到硬件容量的71%.
  • 用键维度进行计算时间缩放,将D值的广泛范围从立方缩小到线性.

结论:

  • 开发的算法和硬件利用打破了量子化学和材料科学中的当前计算限制.
  • 与严格的U(1) 实现相比,混合方法提供了300-500 TFLOPS的估计有效性能,突出了算法和技术进步之间的协同作用.
  • 这项工作为解决先前难以解决的计算化学和材料科学问题铺平了道路.