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相关概念视频

Multi-input and Multi-variable systems01:22

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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机器学习 哈巴德参数与等价神经网络

Martin Uhrin1,2, Austin Zadoks1, Luca Binci1,3

  • 1Theory and Simulation of Materials (THEOS), and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

npj computational materials
|January 28, 2025
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概括

我们开发了一个机器学习模型来预测复杂材料的哈巴德U和V参数. 这种方法可以显著加快计算速度,同时保持高精度,有助于材料的发现.

关键词:
计算方法 计算方法电子属性和材料的电子属性和材料.理论化学是一种理论化学.

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科学领域:

  • 计算材料科学科学 计算材料科学
  • 量子化学 是一个量子化学.
  • 机器学习在物理学中的应用

背景情况:

  • 使用扩展的哈伯德函数 (DFT+U+V) 的密度函数理论准确地描述了具有过渡金属或稀土元素的复杂材料.
  • 准确预测DFT+U+V需要精确的现场 (U) 和现场间 (V) 哈伯德参数,通常通过计算昂贵的第一原则计算来确定.
  • 现有的参数确定方法包括半经验调整或严格但耗时的初始计算.

研究的目的:

  • 开发一种机器学习模型,以快速准确地预测哈伯德U和V参数.
  • 为了规避与传统的DFT+U+V参数计算相关的计算成本.
  • 通过高通量计算选加速材料发现和设计.

主要方法:

  • 利用在原子职业矩阵上训练的等价神经网络作为描述符.
  • 描述器捕获电子结构,局部化学环境和氧化状态.
  • 通过在密度函数扰动理论 (DFPT) 中自相一致的线性响应计算计算的哈伯德参数的有针对性的预测.

主要成果:

  • 机器学习模型实现了Hubbard U的3%和Hubbard V参数的5%的平均绝对相对误差.
  • 该模型使用来自12种不同的材料的数据进行训练,在不同的晶体结构和组成中展示了强度.
  • 预测接近DFPT计算的准确性,但计算开销显著减少.

结论:

  • 开发的机器学习模型为预测哈巴德参数提供了一个计算效率高,准确的替代方案.
  • 它的高可转移性促进了技术应用的加速材料发现和设计.
  • 这种方法绕过了昂贵的自相一致的DFT或DFPT协议,使材料设计更快.