| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Uncertainty Quantification in Data-Driven Materials and Process Design
|
| Presentation Title |
Quantifying Uncertainty in Atomistic Exploration |
| Author(s) |
Thomas Swinburne |
| On-Site Speaker (Planned) |
Thomas Swinburne |
| Abstract Scope |
Uncertainty in atomistic simulation arises from cohesive model form and incomplete sampling. The latter is challenging to quantify when targeting observables which depend on unknown and rare thermally activated mechanisms (e.g. diffusion), as any estimate will be vulnerable to the discovery of new mechanisms. I will discuss recent efforts to rigorously quantify sampling uncertainties, producing robust kMC models or reaction-diffusion equations. Using examples of defect diffusion in alloys and structural transformations of atomic clusters, I will show how the bounding and propagation of sampling uncertainty depends critically on both the quantity of interest and the sampling method. Some of these ideas are exploited by the massively parallel TAMMBER code, which autonomously manages sampling effort such that the target uncertainties reduce maximally fast. If time allows I will also discuss how this same framework can be used to propagate and target model form uncertainty. Papers and code: https://tomswinburne.github.io |