The research activity of the Institute of Physics covers five main domains:
- Quantum Science and Technology
- Condensed Matter Physics
- Biophysics and complex systems
- Particle and astrophysics
- Physics for energy
Towards the design of molecular materials: from many-body methods to enhanced density functional approximations
New technologies are made possible by new materials, and until recently new materials could only be discovered experimentally. However, approaches based on the fundamental laws of quantum mechanics are now integrated to many design initiatives in academia and industry, underpinning efforts such as the Materials Genome initiative or the computational crystal structure prediction (CSP ). The latest CSP blind test organized by the Cambridge Crystallographic Data Center  revealed two major remaining challenges:
(i) Crystal polymorphs are often separated by just a few kJ/mol, exceeding the accuracy of standard density functional approximations (DFAs).
(ii) Dealing with a vast search space, in particular for molecules with increased flexibility, one has to sample about 1 Mio possible crystal structures.
Recent algorithmic developments in Quantum Monte-Carlo make it feasible to molecular crystals and we are now able to predict static lattice energies with potentially sub-chemical accuracy . On the other hand, cost-effective electronic structure methods will be presented that gain up to four orders of magnitude in computational speed compared to traditional DFAs and are suited for optimizing a huge number of putative crystal structures . Promising applications to the CSP of pharmaceutical-like molecules have been demonstrated recently . A perspective on employing machine learning techniques in the CSP context will be discussed.
 S. L. Price, JGB, Molecular Crystal Structure Prediction; Elsevier Australia, 2017.
 A. M. Reilly, R. I. Cooper, C. S. Adjiman, S. Bhattacharya, A. D. Boese, JGB, P. J. Bygrave, R. Bylsma, J.E. Campbell, R. Car, et al. Acta. Cryst. B 2016, 72, 439.
 A. Zen, JGB, J. Klimeš, A. Tkatchenko, D. Alfè, A. Michaelides, Proc. Natl. Acad. Sci. USA 2018, 115, 1724.
 E. Caldeweyher, JGB, J. Phys.: Condens. Matter 2018, 30, 213001.
 L. Iuzzolino, P. McCabe, S. L. Price, JGB, Faraday Discuss. 2018, 211, 275.
About the speaker — Following his Diplom in physics at Heidelberg University, Dr. Brandenburg completed his dissertation in Theoretical Chemistry in 2015. He moved to the University College London as a visiting lecturer funded by the Alexander von Humboldt foundation. In 2018, he moved back to Germany, where he currently continues his research at the University of Göttingen. His research involves computer simulations of molecular crystals with specific focus on the prediction of organic crystal structures and their properties. He develops and applies simplified density functional based electronic structure approaches as well as many-body methodologies. Dr. Brandenburg has been awarded numerous early career prices, among them the PhD price of the university society Bonn for the best thesis over all disciplines. His research has been published in over 40 peer-reviewed articles. He is partner of the ERC consortium NanoSolveIT and contributor of an INCITE 2019 project funded by the U.S. Department of Energy.
By: Jan Gerit Brandenburg (University of Göttingen & University College London)
The theoretical study of realistic systems of interest in life sciences or nanotechnology is possible only if the contrasting goals of high accuracy and low computational costs are met. Despite its success and increased popularity, DFT fails at times in the ultimate goal of being a predictive tool. For this reason, development of alternative electron correlation methods is still a very active area of research in Quantum Chemistry. This talk starts with an overview on some of the computational techniques developed around the paradigm of multiconfigurational WFT. Several key elements are discussed. First, how such methods can be useful for the study of strong correlation in molecules. Second, the way we can build robust multiscale approaches on top of WFT in order to dissect electron correlation effects into components of different characteristic length-scale. The talk will end with a presentation of some ideas aimed at the design of a new class of DFT functionals with the help of information from WFT.
About the speaker — Francesco Aquilante holds a PhD in theoretical chemistry from Lund University with a thesis on the development of the so-called ab initio Density Fitting from Cholesky Decomposition approximation.After a postdoctoral stay at Geneva University, he became an independent researcher at Uppsala University and then obtained a grant from the Italian Research Ministry to carry on his work at the University of Bologna. He is now completing a second research stay at Geneva University. One of the major contributors to the MOLCAS quantum chemistry software, his expertise spans from multiscale techniques for excited state calculations to the treatment of strong correlation in molecules through electron correlation methods and novel density functional approximations.
By: Francesco Aquilante (University of Geneva)
Development and validation of interatomic potentials and application to the simulation of phase transformations
I will present five short stories on the derivation of models of the interatomic interaction from Density Functional Theory (DFT), the validation of potentials and their application to the simulation of phase transformations. I will show how a systematic coarse graining of the electronic structure from DFT to the tight-binding approximation and analytic Bond-Order Potentials leads to magnetic potentials that capture the subtle interplay between magnetism and phase stability in iron. Further I will introduce the atomic cluster expansion as a formal many-atom expansion with an accuracy and transferability comparable to current machine learning approaches As different researchers typically have a different focus when developing potentials, interatomic potentials have various application ranges. I will present automated high-throughput calculations to validate a large number of interatomic potentials against DFT and to discern their application ranges I will then discuss the application of magnetic Bond-Order Potentials to simulating finite temperature magnetism in iron, in particular the ferromagnetic to paramagnetic phase transformation, the alpha-gamma transition and the prediction of mechanical properties. I will further summarize atomic simulations for phase stability, nucleation and solid-solid transformations with relevance to high-temperature materials.
About the speaker — Ralf Drautz received his Diploma in Physics (with distinction) from the Universität Stuttgart, Germany in 1998. He was a PhD student at the Max-Planck-Institut für Metallforschung, Stuttgart, Germany and received his PhD degree (Dr. rer. nat., summa cum laude) in 2003. Ralf Drautz was a research fellow (2003-2004), and a Senior Research Fellow and Materials Modelling Laboratory Research Fellow (2005-2008) at the University of Oxford. Since 2008, he has been Chair Professor at Ruhr-Universität Bochum in the Department of Physics and Astronomy, as well as the Director at the Interdisciplinary Centre for Advanced Materials Simulation (ICAMS).
By: Ralf Drautz (ICAMS, Ruhr-Universität Bochum)
The MARVEL Junior Seminars aim at intensifying interactions between the MARVEL Junior scientists belonging to different research groups (i.e. PhD & Postdocs either directly funded by the NCCR, or as a matching contribution). The EPFL community interested in MARVEL research topics is very welcome to attend.
Each seminar consists of two 25-minute presentations, followed by time for discussion.
Pizza will be served at 11:45 in front of the auditorium and you are also cordially invited after the seminar at 13:30 for coffee and dessert to continue the discussion with the speakers.
Machine learning potentials for liquid alkanes
Intrinsic ductility as a precursor to ductile fracture
By: Max Veit (EPFL, COSMO) & Predrag Andric (EPFL, LAMMM)
Deep learning has been immensely successful at a variety of tasks, ranging from classification to artificial intelligence. Yet why it works is unclear. Learning corresponds to fitting training data, which is implemented by descending a very high-dimensional loss function. Two central questions are (i) since the loss is a priori not convex, why doesn't this descent get stuck in poor minima, leading to bad performance? (ii) Deep learning works in a regime where the number of parameters can be larger, even much larger, than the data to fit. Why does it lead to very predictive models then, instead of overfitting?
Here I will discuss an unexpected analogy between the loss landscape in deep learning and the energy landscape of repulsive ellipses, that supports an explanation for (i). If times permit I will discuss (ii), more specifically the surprising finding that predictive power continuously improves by adding more parameters.
Two-dimensional crystals of semi-metallic van der Waals materials hold much potential for the realization of novel phases, as exemplified by the recent discoveries of a polar metal in few-layer 1T’-WTe2 and of a quantum spin Hall state in monolayers of the same material. Understanding these phases is particularly challenging because little is known from experiment about the momentum space electronic structure of ultrathin crystals. In this talk, I will discuss direct electronic structure measurements of exfoliated mono- bi- and few-layer 1T’-WTe2 by laser-based micro-focus angle resolved photoemission. This is achieved by encapsulating a flake of WTe2 comprising regions of different thickness with monolayer graphene. Our data support the recent identification of a quantum spin Hall state in monolayer 1T'-WTe2 and reveal strong signatures of the broken inversion symmetry in the bilayer. We finally discuss the sensitivity of encapsulated samples to contaminants following exposure to ambient atmosphere.
About the research of the speaker: https://dqmp.unige.ch/baumberger/