Uncertainty Quantification in Atomistic Simulations of Silicon using Interatomic Potentials
arXiv:2402.15419 · doi:10.1063/5.0214590
Abstract
Atomistic simulations often rely on interatomic potentials to access greater time- and length- scales than those accessible to first principles methods such as density functional theory (DFT). However, since a parameterised potential typically cannot reproduce the true potential energy surface of a given system, we should expect a decrease in accuracy and increase in error in quantities of interest calculated from simulations. Quantifying the uncertainty on the outputs of atomistic simulations is thus an important, necessary step so that there is confidence in results and available metrics to explore improvements in said simulations. Here, we address this research question by forming ensembles of Atomic Cluster Expansion (ACE) potentials, and using Conformal Prediction with DFT training data to provide meaningful, calibrated error bars on several quantities of interest for silicon: the bulk modulus, elastic constants, relaxed vacancy formation energy, and the vacancy migration barrier. We evaluate the effects on uncertainty bounds using a range of different potentials and training sets.
arXiv comment15 pages, 8 figures
References in corpus (17)
- Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
- Moment Tensor Potentials: a class of systematically improvable interatomic potentials
- A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
- Machine learning a general purpose interatomic potential for silicon
- MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
- Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
- Uncertainty estimation for molecular dynamics and sampling
- Deep Ensembles vs. Committees for Uncertainty Estimation in Neural-Network Force Fields: Comparison and Application to Active Learning
- Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
- Tensor-reduced atomic density representations
- Exploring the robust extrapolation of high-dimensional machine learning potentials
- Robustness of Local Predictions in Atomistic Machine Learning Models
- A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
- Bayesian, frequentist, and information geometric approaches to parametric uncertainty quantification of classical empirical interatomic potentials
- Uncertainty quantification for classical effective potentials: an extension to potfit
- Uncertainty quantification design principles for machine learning interatomic potentials: lessons learned from hierarchical Bayesian inference
- Uncertainty Quantification and Propagation in Atomistic Machine Learning