High-performance computer cluster.

Condensed Matter Theory

High-performance computer cluster.
Image: Jan-Peter Kasper (University of Jena)
Silvana BOTTI Silvana BOTTI Image: Private

Prof. Dr. Silvana BOTTI

Email: silvana.botti@uni-jena.de
Phone: +49 3641-9-47150

Professor Silvana Botti holds the Chair of Theoretical Solid State Physics at the Friedrich Schiller University Jena. She is also a member of the Michael Stifel Center Jena and associate editor of npj Computational Materials.

The Condensed Matter Theory Group works on the theoretical development and numerical implementation of many-body approaches for the description of electronic excitations. The tools used are based on density functional theory and many-body perturbation theory. Examples of applications are the simulation of spectroscopic properties of "real" materials of technological interest, that can range from simple bulk crystals to non-stoichiometric, doped, alloyed compounds, or to nanostructured materials and interfaces. At the same time, a "materials by design" approach based on global structural prediction and high-throughput calculations is followed to propose novel materials, that are then further characterized with the same techniques used for known materials. Present research activities are particularly focused on the understanding of electronic properties of materials for energy and the design of new candidate materials for applications in the domain of energy production, storage and saving. The group has a network of theoretical and experimental collaborators in Germany and outside Germany. In particular, the group is member of the European Theoretical Spectroscopy Facility (ETSF, www.etsf.eu).

Research Areas

Professor Botti’s research activities focus on the computational design of new technological materials, as well as on the development and application of many-body treatments for theoretical spectroscopy. An example of applications is the simulation from first-principles (i.e., without using experimental parameters) of the response of a material to an external perturbation, such as incoming electromagnetic radiation or incoming particles. The materials studied can range from bulk crystals to doped and alloyed compounds, or to nanostructures and interfaces.

Teaching Fields

Professor Botti teaches advanced theory courses for undergraduate and Master’s degree students of Physics and she is at present Dean of Studies of the Faculty of Physics and Astronomy. Botti’s group offers Bachelor‘s degree, Master‘s degree, and PhD projects on a variety of topics in the area of solid state theory, nanophysics, computational materials design and machine learning.

Research Methods

The Botti group develops and applies first-principles methods for the study of electronic excitations in complex materials for optoelectronics and energy. The approaches used are based on density functional theory, many-body perturbation theory and machine learning.

Recent Research Results

The Condesed Matter Theory group works on the forefront theoretical approaches for excited states, contributing to the development of the theory, its implementation in efficient numerical codes and its application to a variety of systems of technological relevance. Prof. Botti‘s recent research activity has been mainly devoted to the understanding of electronic properties of materials for energy, with a particular focus on photovoltaics, and to the design of new candidate materials for applications in energy production, storage and saving. New materials, that have not been synthesized yet, are proposed using ab initio global structural prediction and high-throughput calculations. The thermodynamically stable compounds can then be precharacterized in silico using the same techniques employed for already known materials [1]. One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. The Botti’s group is contributing with pioneering work to this exciting field [2, 3].

[1] Körbel et al., j. Mater. Chem. A 6, 6463 (2018).
[2] Schmidt et al., npj Comput. Mater. 5, 83 (2019).
[3] Borlido et al., NPJ Comput. Mater. 6, 96 (2020).
[4] Fadaly et al., Nature 580, 205 (2020).

link to the Condensed Matter Theory Group

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