Practical discretisation error estimates for data-driven materials modelling (Swiss National Science Foundation, from 2025)
National competence centre in research MARVEL, École Polytechnique Federale Lausanne (EPFL) Swiss national research centre on Computational Design and Discovery of Novel material including researchers in experimental and computational physics and chemistry as well as computer science. The goal is to bridge between communities and accelerate the state of the art of materials discovery. Our group joined the centre in May 2023 for the third funding phase in the pillar concerning Digital Infrastructure of Open Simulation and Data. Key aim is to integrate our software efforts surrounding efficient black-box algorithms with the high-throughput and pre-exascale platforms currently under development at MARVEL.
Center for Exascale Simulation of Material Interfaces in Extreme Environments, Massachusetts Institute of Technology. Research initiative advancing state-of-the-art first-principle simulations by connecting method development with (a) advances in programming languages, compiler technologies and performance engineering tools and (b) underpinning them with rigorous approaches to statistical inference and uncertainty quantification (UQ). With respect to the former aspect my main interaction is with the JuliaLab of Alan Edelman and on the latter I collaborate with Youssef Marzouk on uncertainty quantification in first-principles simulations.
Extreme-scale Mathematically-based Computational Chemistry, Sorbonne University, Inria Paris and École des Ponts ParisTech. Interdisciplinary initiative to advance the state of the art in first-principle simulations by integrating recent advances in numerical linear algebra (e.g. low-rank factorisations, randomised methods, GPU acceleration) and numerical analysis (e.g. a posteriori error estimation) into state-of-the-art molecular-dynamics and electronic-structure methods. Continuing from my PostDoc I participate in the development of black-box density-functional theory methods and a posteriori error estimates, mainly working with Eric Cancès and Antoine Levitt.
Eric Cancès (Numerical analysis, École des Ponts ParisTech): A posteriori error analysis for electronic-structure simulations.
Youssef Marzouk (Uncertainty quantification, Massachusetts Institute of Technology): Uncertainty quantification in density-functional theory.
Antoine Levitt (Numerical analysis, University of Paris-Saclay): Reliable black-box algorithms for electronic structure simulations.
Andre Laestadius (Applied mathematics, Oslo Met University): Mathematical formulation of density-functional theory.
Matteo Rizzi(Correlated quantum systems, Universität Köln): Reduced basis methods for correlated quantum spin systems.
Benjamin Stamm (Numerical analysis, Universität Stuttgart): Reduced basis methods for quantum spin systems & solvation models
Stefan Wessel(Correlated quantum systems, RWTH Aachen University): Reduced basis methods for correlated quantum spin systems.
