Coarse-Graining and Forecasting Atomic Material Simulations with Descriptors - Archive ouverte HAL Access content directly
Journal Articles Physical Review Letters Year : 2023

Coarse-Graining and Forecasting Atomic Material Simulations with Descriptors

Abstract

Atomic simulations of materials require significant resources to generate, store, and analyze. Here, descriptor functions are proposed as a general latent space for atomic structures, ideal for use in large-scale simulations. Descriptors can regress a broad range of properties, including character-dependent dislocation densities, stress states, or radial distribution functions. A vector autoregressive model can generate trajectories over yield points, resample from new initial conditions and forecast trajectory futures. A forecast confidence, essential for practical application, is derived by propagating forecasts through the Mahalanobis outlier distance, providing a powerful tool to assess coarse-grained models. Application to nanoparticles and yielding of nanoscale dislocation networks confirms low uncertainty forecasts are accurate and resampling allows for the propagation of smooth property distributions. Yielding is associated with a collapse in the intrinsic dimension of the descriptor manifold, which is discussed in relation to the yield surface.
No file

Dates and versions

hal-04332114 , version 1 (08-12-2023)

Licence

Copyright

Identifiers

Cite

T D Swinburne. Coarse-Graining and Forecasting Atomic Material Simulations with Descriptors. Physical Review Letters, 2023, 131 (23), pp.236101. ⟨10.1103/PhysRevLett.131.236101⟩. ⟨hal-04332114⟩
5 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More