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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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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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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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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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自相一致性错误纠正精确的机器学习潜力从变化的蒙特卡洛.

Giacomo Tenti1, Kousuke Nakano2,3, Michele Casula4

  • 1International School for Advanced Studies (SISSA), Via Bonomea 265, 34136 Trieste, Italy.

Journal of chemical theory and computation
|September 24, 2025
PubMed
概括
此摘要是机器生成的。

变量蒙特卡罗 (VMC) 训练数据中的自一致性错误 (SCE) 可以损害机器学习的原子间潜力 (MLIP). 纠正这种偏差可以显著提高分子动力学模拟的MLIP精度.

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科学领域:

  • 计算材料科学 计算材料科学
  • 量子化学是一种量子化学.
  • 机器学习是机器学习.

背景情况:

  • 变量蒙特卡罗 (VMC) 是一种强大的训练机器学习原子间潜力 (MLIP) 的方法.
  • VMC训练集通常使用部分优化的波函数 (WF) 来降低计算成本.
  • 在WF中,冷变量参数引入自我一致性误差 (SCE),偏向力和压力.

研究的目的:

  • 为了证明SCE对MLIP准确性的不利影响.
  • 将一种新的SCE校正方法应用于VMC训练数据.
  • 为了提高MLIPs用于分子动力学 (MD) 模拟的可靠性.

主要方法:

  • 使用VMC生成MLIP的训练数据.
  • 实施SCE校正VMC波函数与结的Kohn-Sham轨道.
  • 在未经纠正和经过SCE纠正的VMC数据上培训MLIP.
  • 执行MD模拟来评估MLIP性能和物理可观测值.

主要成果:

  • 证明自我一致性错误 (SCE) 对MLIP准确性产生负面影响,使用高压作为测试案例.
  • 将SCE校正应用于VMC培训集显著提高了MLIP质量.
  • 在SCE纠正数据上训练的MLIP接近那些在完全优化的WF上训练的人的准确性.
  • MD模拟证实,经过SCE校正的MLIP产生了更可靠的物理可观测值.

结论:

  • 开发的框架有效地纠正了VMC培训数据中的自我一致性错误.
  • 这种校正可以生成高质量的MLIP,适合准确的MD模拟.
  • 这种方法有助于创建更大,更可靠的基于VMC的培训数据集.