ATMIN

Atomistic processes at material interfaces

The interface is the boundary where two different materials meet. An important example of a material interface which is detrimental to a device’s operation is the solid-electrolyte interphase, a layer that grows within the contact region between electrolyte and electrode in batteries, negatively affecting their reliability. Atomistic modeling, which offers a route to obtain a deep understanding of these processes, is rapidly evolving in the midst of a machine learning and artificial intelligence driven revolution. Yet, the ultimate test for any theory and simulation is experiment, and achieving experimental agreement and incorporating experimental data into these atomistic simulations is the next frontier in atomistic modeling. 

ATMIN proposes an integrated approach that combines experiment and simulation. The goal is to understand the structure of material interfaces with atomistic detail. The project leverages the predictive power of atomistic machine learning, pushing beyond the current state of the art. It utilizes world-class computing infrastructure available through the EuroHPC ecosystem. The ATMIN consortium in cooperation with top international collaborators will strive to bridge the gap between reality and simulation for the characterization of material interfaces at the nanoscale. CSC role in the project is to support the Aalto group in porting and optimizing the TurboGAP modeling software to LUMI and GPUs in general.

This project has received funding from the Research Council of Finland​​​​​​​ under funding decision No 364779.