Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information
Daniel Schwalbe-Koda1,2, Sebastien Hamel3, Babak Sadigh3
1Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA. dskoda@ucla.edu.
Nature Communications
|April 29, 2025
Summary
We developed a model-free framework to quantify information in atomistic simulations using information entropy. This approach enhances machine learning potential development and enables reliable uncertainty quantification for simulations.
Area of Science:
- Atomistic machine learning
- Computational materials science
- Data-driven modeling
Background:
- Atomistic machine learning (ML) often uses unsupervised learning or model predictions for data analysis.
- Accurate information description is crucial for training sets, uncertainty quantification (UQ), and extracting physical insights.
Purpose of the Study:
- Introduce a rigorous, model-free theoretical framework to quantify information content in atomistic simulations.
- Provide a general tool for data-driven atomistic modeling.
Main Methods:
- Quantify information content using the information entropy of atom-centered environments.
- Develop a model-free UQ method based on this information entropy framework.
Main Results:
- Information entropy explains heuristics in ML potential development, including training set size and dataset optimality.
- The proposed UQ method reliably predicts epistemic uncertainty.
- The method effectively detects out-of-distribution samples and rare events like nucleation.
Conclusions:
- The developed framework offers a powerful, model-free tool for analyzing information in atomistic simulations.
- This approach integrates machine learning, simulations, and physical explainability for enhanced data-driven modeling.
Related Concept Videos
01:10Propagation of Uncertainty from Systematic Error
345
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...
345
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
25
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
25
00:59Propagation of Uncertainty from Random Error
500
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...
500
00:59Uncertainty: Overview
387
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.
387
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
13
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
13
00:54Uncertainty: Confidence Intervals
2.9K
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...
2.9K