| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Uncertainty Quantification in Data-Driven Materials and Process Design
|
| Presentation Title |
Bayesian Estimation and Active Learning of Data-driven Interatomic Potentials for Propagation of Uncertainty through Molecular Dynamics |
| Author(s) |
Dallas Foster |
| On-Site Speaker (Planned) |
Dallas Foster |
| Abstract Scope |
Data-driven interatomic potentials represent a compelling class of techniques for simulating and modeling the atomic interactions of large-scale materials. Accurate linear techniques like the Spectral Neighbor Analysis Potential (SNAP) and the Atomic Cluster Expansion (ACE) derive simple relationships between atomic configurations and their potential energy surface but tend to suffer from extrapolation errors and instabilities during long-time molecular simulations. Bayesian methodologies and active learning strategies seek to mitigate these generalization errors. In Bayesian parameter estimation, generalizability is dependent on ensuring that model densities are representative of physical principles, not solely informed by learned statistical patterns that exist in the training data. We discuss how non-Gaussian assumptions in parameter estimation can make potentials more robust, and how these Bayesian methodologies intersect with components of active learning that depend on having calibrated notions of uncertainty: sampling diverse configurations for training and indicating how trustworthy learned potentials are during molecular dynamics simulations. |