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00:59Uncertainty: Overview
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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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00:59Propagation of Uncertainty from Random Error
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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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01:10Propagation of Uncertainty from Systematic Error
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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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00:54Uncertainty: Confidence Intervals
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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02:07Improving Translational Accuracy
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02:07Improving Translational Accuracy
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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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Leveraging Uncertainty from Deep Learning for Trustworthy Material Discovery Workflows.
Jize Zhang1, Bhavya Kailkhura1, T Yong-Jin Han2
1Center for Applied Scientific Computing, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.
ACS Omega
|May 31, 2021
Summary
Predictive uncertainty in deep neural networks helps material scientists determine data needs, identify confusing samples, and detect out-of-distribution data, enhancing machine learning model reliability.
Area of Science:
- Materials Science
- Machine Learning
- Deep Learning
Background:
- Machine learning (ML) applications in materials science often face challenges with data requirements and model reliability.
- Deep neural networks (DNNs) can provide predictions but lack clear indicators of their confidence in these predictions.
- Understanding the uncertainty of DNNs is crucial for dependable material application workflows.
Purpose of the Study:
- To demonstrate how predictive uncertainty in DNNs can address key challenges in ML-based material science.
- To enable users to determine optimal training dataset sizes for desired classification accuracy.
- To introduce methods for detecting and handling ambiguous or out-of-distribution data samples.
Main Methods:
- Leveraging predictive uncertainty quantification from deep neural networks.
- Developing an uncertainty-guided decision referral system for sample classification.
- Utilizing scanning electron microscope (SEM) image-derived microstructure data as a use case.
Main Results:
- Predictive uncertainty effectively guides the determination of necessary training data size for specific classification accuracies.
- The uncertainty-guided referral system successfully identifies and flags confusing samples, preventing erroneous decisions.
- Predictive uncertainty accurately detects out-of-distribution test samples, including those with altered image acquisition or synthesis conditions.
Conclusions:
- Uncertainty-aware deep learning significantly enhances the performance and dependability of classification models in materials science.
- Predictive uncertainty provides a robust mechanism for managing data variability and improving ML model robustness.
- This approach offers practical solutions for material scientists using ML in their workflows.