Machine learning for atomistic simulations of materials


Talk by Christoph Dellago (Faculty of Physics)

Atomistic computer simulations of condensed matter systems are challenging for several distinct but related reasons. For large systems, the accurate calculation of energies and forces needed in molecular dynamics simulations may be computationally demanding, particularly if electronic structure calculations are used for this purpose. Other difficulties arising in the dynamical simulation of condensed matter processes consist in detecting local structures characteristic for stable or metastable phases and in identifying important degrees of freedom that capture the essential physics of the process under study. In this talk, I will discuss how these problems can be addressed using machine learning approaches.

Where and when?

11 June 2021, 12.00 CEST
Zoom-Meeting-ID: 954 8495 6002
Password: dsunivie21