A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning.

ABSTRACT: Machine learning models are increasingly used to predict material properties and accelerate atomistic simulations, but the reliability of their predictions depends on the representativeness of the training data. We present a scalable, GPU-accelerated uncertainty quantification framework based on k-nearest-neighbor kernel density estimation (KDE) in a PCA-reduced descriptor space. This method efficiently detects sparsely sampled regions in large, high-dimensional data sets and provides a transferable, model-agnostic uncertainty metric without requiring retraining costly model ensembles. The framework is validated across diverse case studies varying in (i) chemistry, (ii) prediction models (including foundational neural network), (iii) descriptors used for KDE estimation, and (iv) pro

SUBMITTER: Willimetz D 

PROVIDER: S-EPMC12557357 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature