Scientific Report for the
Theoretical Spectroscopy Lectures
March 21-25, 2022
CECAM-HQ-EPFL, Lausanne, Switzerland
The aim of the school was to give a deep introduction to the theoretical and practical aspects of the electronic excitations which are probed by experimental techniques such as optical absorption, EELS, and photoemission (direct or inverse). From the theory point of view, excitations and excited state properties are out of the reach of density-functional theory (DFT), which is a ground-state theory. In the last thirty years, other ab-initio theories and frameworks, which are able to describe electronic excitations and spectroscopy, have become more and more used: time-dependent density-functional theory (TDDFT) and many-body perturbation theory (MBPT) or Green’s function theory (GW approximation and Bethe-Salpeter equation BSE). In fact, computational solutions and codes have been developed in order to implement these theories and to provide tools to calculate excited state properties. The present school focused on these points, covering theoretical, practical, and also numerical aspects of TDDFT and MBPT, non-linear response, and real-time spectroscopies. For the first time, this year we also covered theoretical aspects of magnetic excitations. Finally, a large part of the school was devoted to the codes implementing such theories (ABINIT, 2Light, DP, EXC). Continue reading Report on the Theoretical Spectroscopy Lectures →
Machine learning potentials have now established themselves as a method of choice in many atomistic simulation projects. This tutorial workshop was aimed at young and early-career researchers who are interested in using machine learning potentials in their work, but are unsure of where to start or of how feasible the proposed application would be.
While the field continues to produce new theoretical and methodological advances, there is now a large class of systems that can be treated with existing, established methods. The main issues now for new researchers entering the field are, first, choosing between the many different machine learning methods (and correspondingly many software packages) available, and second, learning about simulation workflows and best practices that are often undocumented, unwritten “common knowledge”.
The workshop was designed with two main aims: First, to give these researchers a solid introduction in the basic scientific techniques of designing, fitting, and validating a machine learning potential for a new system. Second, to provide a platform for young researchers interested in using machine learning potentials in their work to connect to those involved in developing methods for machine learning potentials, in order to accelerate the adoption of machine learning techniques in the wider atomistic simulation community. Continue reading Report on the “ML-IP 2021” workshop (Young and Early-career Researchers’ Tutorial on Machine Learning Interatomic Potentials) →