Bio: Michael Herbst obtained a PhD in Theoretical Chemistry from Heidelberg University in 2018, after which he moved on to two postdoctoral research stays in Applied Mathematics with Éric Cancès (École des Ponts, France) and Benjamin Stamm (RWTH Aachen, Germany). Since March 2023, he has been a tenure-track assistant professor in the Institute of Mathematics and the Institute of Materials at EPFL. His current research spans broadly in the field of materials simulations concerning numerical error control and uncertainty quantification of first-principle simulations, as well as the propagation of such errors during inverse materials design or when training machine learning models.

Abstract: Reliable algorithmic differentiation techniques offer great promise for the inverse design of materials and functionals, as well as the propagating uncertainties from functionals to DFT quantities of interest. Over the past years, considerable effort has been spent on equipping the density-functional toolkit (DFTK, https://dftk.org) with algorithmic differentiation capabilities. Prof. Herbst will present some of the required algorithmic developments, e.g. to efficiently compute such DFT derivatives in numerically challenging metallic systems. Furthermore, he will highlight the conceptual difficulties associated with applying AD to plane-wave DFT and discuss our recent results, which demonstrate the current state of AD in DFTK for error estimation, inverse design, and implementing new functionality.