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相关概念视频

Porosity and Absorption of Aggregate01:20

Porosity and Absorption of Aggregate

Aggregates contain pores of varying sizes; while some are completely enclosed within the particles, others open onto the surface, allowing water to penetrate. The porosity of aggregates is a major factor contributing to the overall porosity of concrete, given that aggregates constitute about three-quarters of concrete's volume.
When all pores in an aggregate are filled with water, the aggregate is considered saturated and surface-dry. If left in dry air, water will evaporate until the aggregate...

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Determination of the Settling Rate of Clay/Cyanobacterial Floccules
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关于各种机器学习交互潜力的数据集可移植性的调查,用于铁粉粘土的铁粉粘土.

Chloe Sanz1, Colin Bousige2, Pierre Mignon1

  • 1Université Claude Bernard Lyon 1, CNRS, iLM UMR 5306, Villeurbanne F-69100, France.

The journal of physical chemistry. A
|December 18, 2025
PubMed
概括
此摘要是机器生成的。

这项研究表明,机器学习交互潜力 (MLIP) 可以准确预测材料特性,强调具有代表性的数据集和描述器选择对于计算材料科学的可靠结果的重要性.

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科学领域:

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

背景情况:

  • 神经网络潜力 (NNP) 越来越多地用于模拟材料特性.
  • 准确的描述器对于捕捉高维机器学习交互潜力 (MLIP) 中的原子相互作用至关重要.
  • 了解架构和描述符对NNP准确性的影响对于可靠的预测至关重要.

研究的目的:

  • 调查架构和描述符对MLIP准确性的影响.
  • 将不同MLIP的性能与参考计算进行比较.
  • 评估后期增加分散校正的影响.

主要方法:

  • 开发和训练了四个MLIP,使用以原子为中心的对称函数,具有不同的描述符 (嵌入,注意力面具,消息传递).
  • 根据代表性数据集验证了MLIP,并与PBE-D3参考结果进行了比较.
  • 后期将Grimme的D3分散校正应用于PBE数据训练的MLIP.

主要成果:

  • 所有研究的MLIP都准确地复制了PBE-D3的能量和力量,无论描述符的选择如何.
  • 在不同的MLIP中观察到结构参数,脱皮能量和振动光谱的准确性相似.
  • 后期D3校正提高了弹性和剥皮能量的精度,并提高了稳定性.

结论:

  • 构建一个具有代表性的数据集对于在MLIP中实现所需的准确性至关重要.
  • 经过适当数据的训练,MLIP可以可靠地预测各种材料特性.
  • 后期分散校正是一种有效的策略,可以提高特定属性的MLIP精度.