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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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多主管委员会能够直接预测原子基础模型的不确定性.

Hubert Beck1, Pavol Simko1, Lars L Schaaf2,3

  • 1Charles University, Faculty of Mathematics and Physics, Ke Karlovu 3, 121 16 Prague 2, Czech Republic.

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概括

本研究介绍了一个委员会神经网络的潜力,使用MACE进行材料建模中的高效不确定性预测. 这种方法准确地估计了模型不确定性,并使基础模型的培训数据减少得很大.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 机器学习 机器学习

背景情况:

  • 机器学习潜力对于原子材料建模至关重要.
  • 高效的不确定性预测仍然是积极学习和错误分析的挑战.

研究的目的:

  • 实施一个委员会神经网络的潜力,用于使用MACE的消息传递架构.
  • 评估这个委员会模型的不确定性估计能力.
  • 将该方法应用于积极学习和数据集凝聚的基础模型.

主要方法:

  • 利用MACE及其多头机制创建了一个委员会神经网络.
  • 在相同的原子环境描述器上训练了多个输出模块.
  • 将委员会方法应用于MACE-MP-0基础模型,只培训新的输出主管.

主要成果:

  • 证明预测的标准偏差可以作为可靠的不确定性估计.
  • 预测的不确定性和预测力的真实错误之间显示出强烈的相关性.
  • 通过使用主动学习,成功将基础模型的培训集缩小到其原始大小的5%.

结论:

  • 多头委员会方法为机器学习潜力提供了可靠的不确定性估计.
  • 这种方法可以显著减少基础模型的训练数据大小,而不会影响准确性.
  • 在材料建模中实现更高效,更可靠的积极学习工作流.