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The Uncertainty Principle04:08

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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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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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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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Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
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分子机器学习中的不确定性校准:比较证据和组合方法.

Bidhan Chandra Garain1, Max Pinheiro1, Matheus de Oliveira Bispo1

  • 1Aix Marseille University, CNRS, ICR, Marseille, France.

Chemistry (Weinheim an der Bergstrasse, Germany)
|February 4, 2026
PubMed
概括

后期校准对于量子化学中可靠的机器学习至关重要. 它从深度证据回归和深度合集中纠正错误校准的不确定性,使可靠的预测和高效的分子建模成为可能.

关键词:
深度合唱团的合唱团.机器学习是机器学习.分子模拟分子模拟专项校准后的校准.不确定性量化不确定性量化

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科学领域:

  • 量子化学 是一个量子化学.
  • 机器学习 机器学习
  • 计算化学的计算化学

背景情况:

  • 机器学习 (ML) 模型在量子化学中至关重要.
  • ML模型的可靠性取决于不确定性量化 (UQ).
  • 现有的UQ方法,如深度证据回归 (DER) 和深度合集,都有局限性.

研究的目的:

  • 在量子化学中比较DER和UQ的深层组合.
  • 评估后期校准对UQ方法的影响.
  • 在QM9和WS22数据集上评估校准技术的有效性.

主要方法:

  • 应用深度证据回归 (DER) 和深度集合.
  • 使用的后期校准方法:同位素回归 (ISR),标准缩放和GP-Normal.
  • 在QM9和WS22数据集上评估性能,用于分子性质预测.

主要成果:

  • 来自DER和合集的原始不确定性被错误校准.
  • 校准技术成功地将预测的差异与观察到的错误对齐.
  • 校准的DER在QM9.9上改进了高可靠性预测过.
  • 在WS22上,校准合集将冗余的初始评估减少了20%以上,从而增强了积极学习.

结论:

  • 后期校准对于准确的量子化学UQ至关重要.
  • 校准将不确定性估计转化为可操作的见解.
  • 校准的UQ方法确保可靠的预测和资源高效的分子建模.