Prof. Alexandre Tkatchenko
Head of the Research Group
Alexandre Tkatchenko is a Professor of Theoretical Chemical Physics at the University of Luxembourg. He obtained his bachelor degree in Computer Science and a Ph.D. in Physical Chemistry at the Universidad Autonoma Metropolitana in Mexico City. In 2008−2010, he was an Alexander von Humboldt Fellow at the Fritz Haber Institute of the Max Planck Society in Berlin. Between 2011 and 2016, he led an independent research group at the same institute. Tkatchenko has given more than 250 invited talks, seminars and colloquia worldwide, published more than 170 articles in peer-reviewed academic journals (h-index=67), and serves on the editorial boards of Physical Review Letters (APS) and Science Advances (AAAS). He received a number of awards, including elected Fellow of the American Physical Society, the 2021 van der Waals Prize from ICNI, the 2020 Dirac Medal from the World Association of Theoretical and Computational Chemists, the 2011 Gerhard Ertl Young Investigator Award of the German Physical Society, and three flagship grants from the European Research Council: a Starting Grant in 2011, a Consolidator Grant in 2017, and a Proof-of-Concept Grant in 2020. His group pushes the boundaries of quantum mechanics, statistical mechanics, and machine learning to develop efficient methods to enable accurate modeling and new insights into complex materials.
Research Scientist; Team Leader on Quantum Mechanics
Coarse-grained many-body methods based on coupled quantum harmonic oscillators as well as first-principles approaches to study molecules and materials.
Computational condensed matter physics, in particular, first-principles calculations of the electronic structure of three- and two-dimensional crystals as well as description of their transport properties for various phenomena in the field of spintronics and spin caloritronics (like the spin relaxation, the spin and anomalous Hall effect, the spin Nernst effect), including phenomena caused by the geometric phase.
2012-2016 PostDoc, Max Planck Institute of Microstructure Physics, Halle, Germany
2006-2011 PostDoc, Martin Luther University Halle-Wittenberg, Halle, Germany Development and employment of program packages for ab initio calculation of electronic structure and transport properties of solid states, description of physical phenomena related to the field of spintronics and spin caloritronics
1999-2005 Physical-Technical Institute, Ural Branch of Russian Academy of Sciences, Izhevsk, Russia
2004-2005: Senior scientist
1999-2000: Junior research scientist
Development and employment of program packages for calculation of the electronic structure and magnetic properties of solid states, theoretical investigations of their surfaces and electronic response to an applied electric field, transport properties of ultrathin films
1996-1999 Physical-Technical Institute, Izhevsk, Russia, Ph.D.: Solid State Theory (1999)
1991-1996 Udmurt State University, Izhevsk, Russia, M.S.: Theoretical Physics (1996) [with honors]
Research Scientist; Team Leader on Machine Learning
Research Interests: Statistical physics, imaginary-time path integral methods, nuclear quantum effects, ab initio simulations
Senior Researcher January, 2016 - present University of Luxembourg
Postdoctoral Fellow December, 2013 - January, 2016 Fritz Haber Institute of the Max Planck Society
Postdoctoral Fellow July, 2012 - December, 2013 Institute of Theoretical and Computational Chemistry at POSTECH
Junior Research Associate April, 2009 - July, 2012 B. Verkin Institute for Low Temperature Physics & Engineering, National Academy of Sciences of Ukraine
Research Scientist; Team Leader on Biomolecular Modeling
I am interested in biomolecule mechanics and self assembly, in particular I like to observe protein and DNA in action and to understand these versatile molecules in their operation as nanomachines.
Computer simulation, statistical mechanics, thermodynamics, kinetics, rare event statistics and machine learning.
2010 -> present : University of Luxembourg, Research Scientist and Lecturer.
2009 -> 2010 : University of Mainz, Germany. Postdoctoral researcher.
2006 -> 2010 : University of Leeds, UK: PhD student (Physics and Life Sciences).
2001 -> 2006 : Imagination Technologies, UK: Graduate design engineer.
2000 -> 2001 : University of Edinburgh, School of Informatics: MSc student.
1996 -> 2000 : University of Edinburgh, School of Maths and Physical Sciences: BSc student.
University staff directory: https://wwwen.uni.lu/research/fstm/dphyms/people/josh_berryman
Personal Homepage: http://berrymanscience.com
- Current interests:
Study of strongly correlated materials, where the physics is dominated by the interplay between electronic, spin and vibrational degrees of freedom. The goal is to dominate theoretical laws in order to investigate the exotic phases of matter that might emerge in these materials and predict the collective properties of very large numbers of electrons, atoms or molecules to discover new principles and phenomena at the multidisciplinary interfaces of microscopic and macroscopic models.
- Academic History:
2011–2013 - Master Degree in Theoretical Physics (110/110 L), University of Bari, Italy.
2014–2015 - Holder of a Research Grant in Ghost Imaging and Plenoptic Correlation, University of Bari, Italy.
2015–2018 PhD in Physics, IMPMC, Sorbonne Université, Paris, France.
Thesis: "Exotic phenomena in the new frustrated spin ladder Li2O(CuSO4)2" (Supervisors: Guillaume Radtke and Gwenaelle Rousse)
Leonardo Medrano Sandonas
-2018-2019: Research assistant at the Center for Advancing Electronics Dresden, Dresden, Germany.
-2014-2018: Doctor of Engineering, Chair of Materials Science and Nanotechnology, Dresden University of Technology, Dresden, Germany.
-2010-2012: Master in Physics, Condensed matter group, Universidad Nacional Mayor de San Marcos, Lima, Peru.
- Machine learning methods for understanding the chemical space of molecular systems.
- Quantum and classical picture of thermal transport phenomena at the nanoscale.
- Thermoelectric properties of low-dimensional materials.
- Software development for research.
- Development of a quantum Monte Carlo code with geminal and Pfaffian wave functions with GPU acceleration
- Contribution to the project of electron-positron interactions in molecular systems
- Contribution to the project of multiscale approaches for the study of long range interactions
- Quantum Monte Carlo methods
- Density functional theory
- Ab initio wave function based quantum chemistry methods
- Strongly correlated systems.
2017 - 2019 Postdoctoral researcher at the University of Luxembourg
2014 - 2016 Research assistant at the National Research Council, Institute of Nanoscience (Modena)
2015 - PhD in ‘Engineering and Physical-Mathematical Modelling', University of L'Aquila
2011 - MSc in Physics, `Sapienza' - University of Rome
2006 - BSc in Physics, `Sapienza' - University of Rome
My research interests lie in the study of correlated atomic systems in out-of-equilibrium conditions. My current work focuses on the interplay between many-body dispersion interactions and an external driving in simple model systems.
— Density functional theory and Green's functions methods for many-body systems
— First principles calculations of electronic structure and spin transport properties of magnetic nanostructures
— Finite temperature atomistic spin dynamics modelling of ferromagnetic and antiferromagnetic materials
[2015-2019] PhD in Theoretical and Computational Physics at Trinity College Dublin :
“Multi-scale analysis of current-driven spin dynamics in magnetic tunnel junctions”
[2013-2015] MSc in Theoretical and Model Physics, Università di Padova :
“Development of a smart BSE approach for the calculation of optical properties of complex systems”
[2010-2013] BSc in Physics, Università di Padova :
“Quantum entanglement for ultra-cold atoms in optical traps”
The main current research interests concern the role eventually played by classical and quantum electrodynamics interactions in the dynamical organization of biomolecular systems. In this framework, I recently focused my attention on the possible role played by many-body van-der-Waals interactions in the biomolecular allosteric pathways and on how such interactions may contribute to affect the mechanical response of biomolecules in THz and far-IR domain out-of-thermal equilibrium in an aqueous environment. Moreover, I am interested to apply the existing geometrical formulations of quantum mechanics to characterize van-der-Waals dispersive interactions in many-body quantum systems.
Modellization of dynamics and statistical properties of biomolecular systems out-of-thermal equilibrium. Description of quantum optical effects in biomolecular systems. Application of differential geometrical and topological methods for the description of classical Hamiltonian dynamics and phase transitions in classical systems at the thermodynamical equilibrium.
2020 PostDoc, Quantum Biology Laboratory, Howard University: Study of quantum electrodynamics and dispersive interactions in biomolecular complexes
2018-2019 PostDoc, Centre de Physique Theorique, Universite' Aix-Marseille
2016 Ph.D. [Theoretical and Mathematical Physics] Centre de Physique Theorique, Universite' Aix-Marseille: Phase transition theory with applications to BiophysicsImprovement of a Necessity Theorem on the topological origin of phase transitions at thermal equilibrium. Theoretical contributions to fix the problem due to a "counterexample". Theoretical study of Fröhlich-like out-of-equilibrium phase transition in classical systems. Interpretation of THz spectroscopy experiments in biophysics. Numerical investigations on systems of mutually interacting and diffusing biomolecules for the validation of experimental methods
2013 M.Sc. [Theoretical and Mathematical Physics] University of Florence: Theoretical and numerical investigation on diffusion properties of biomolecules for experimental testing of long-range interactions between them
2010 B.Sc. [Physics] Univerisity of Florence: Geometrical description of Hamiltonian Dynamics. Description of classical Hamiltonian dynamics in terms of trajectories in configuration space endowed with Jacobi metric
2017-2021 Postdoc in Korea Institute for Advanced Study (KIAS)
2014-2017 PhD. in String Theory at Université Pierre et Marie Curie
2011-2014 Undergrad and Master degree in Ecole Normale Supérieure (Paris)
-Machine Learning for the study of physico-chemical properties of molecules
-Geometrical and Quantum Field Theory approaches to the study of molecular interactions.
-Machine Learning approach to the study of phase transitions in Statistical Physics models.
-Supersymmetric quantum field theories in various dimensions
-Conformal Field Theories in two dimensions
Loris Di Cairano
- Geometric description of equilibrium phase transitions and Hamiltonian chaos
- Applications of Quantum Field Theory-based methods to condensed matter
- Theoretical methods for studying the emergence of stochastic behaviors in interacting Hamiltonian systems
- Molecular Dynamics simulations of proteins diffusion in lipid membrane
[2018-2021] Ph.D. in Theoretical and Computational Physics, RWTH Aachen University and Forschungszentrum Jülich: Generalized Langevin Equation-based approach for investigating Complex Biological Systems.
[2015-2018] M.Sc., Theoretical Physics, University of Rome, Tor Vergata: Geometric Approach to Nonlinear Hamiltonian systems and Chaos.
[2011-2015] B.Sc. in Physics, University of Rome, Tor Vergata: Tangent Gruppoid and Strict Quantization Deformation.
- Dispersion forces and van der Waals interactions in excited states
- Continuum contribution to van der Waals interactions
- Influence of nonlocality on van der Waals interaction
- Dispersion interactions in thermal electromagnetic fields
Development and application of atomistic modeling methods as well as machine learning techniques for an efficient and accurate modeling of terahertz (THz) spectra of molecular crystal polymorphs.
- Density Functional Theory (DFT) & Time-Dependent Density Functional Theory (TDDFT).
- Ab-initio molecular dynamics (MD) (Born-Oppenheimer MD & Car-Parrinello MD).
- Restricted Open-Shell Kohn Sham (ROKS).
- Classical MD and Monte Carlo simulations.
- Force-Field development.
- Machine learning for predicting proteins' properties.
- Computational studies of the structural, optical, excited-state and mechanical properties of lead halide perovskites.
- Characterisation of the dynamics of proteins as a way to propose therapeutic routes for diseases that are caused by mutations.
- Characterisation of polymer/solid interfaces.
2021-2021: Scientific Research Associate, Swiss Institute of Bioinformatics, University of Basel, Switzerland.
2019-2020: Postdoctoral researcher, Laboratory of Computational Chemistry and Biochemistry, École Polytechnique Fédérale de Lausanne, (EPFL), Switzerland.
2014-2019: PhD, EPFL, Switzerland.
2009-2014: Diploma of Chemical Engineering, National Technical University of Athens, Greece.
Valentin Vassilev Galindo
My current research focuses on the sampling of the Potential Energy Surfaces (PES) of molecules with many and/or complex degrees of freedom in order to collect enough data to develop or improve Machine Learning (ML) models. Then, such models will help us to obtain quantum mechanical information and gain insight on troublesome phenomena happening on the PES by employing Path-Integral Molecular Dynamics, which can become affordable for big systems with the aid of the ML models.
Designing of non-classical chemical compounds (e.g. Planar Hypercoordinate Carbons) and understanding of its chemical bonding and atomic properties.
Extracting chemical concepts from the wave function.
Mechanisms of simple reactions using computational approaches.
2022 Ph.D. in Physics (University of Luxembourg, Luxembourg): "Machine learning force fields: towards modelling flexible molecules
2017 M. Sc. in Physical Chemistry (CINVESTAV – Mérida, Mexico): “Designing planar pentacoordinate carbons”.
2014 B. Eng. in Chemical Engineering (Universidad Veracruzana, Mexico): “Theoretical study and simulation of CO2absorption process using amines”.
- Development of interatomic potentials for large systems using neural networks combined with simple models of long-range physics, such as the many-body dispersion (MBD) method
- Predicting the structure and stability of molecular crystals
- Density functional theory (DFT)
- Time-dependent density functional theory (TDDFT)
- Device physics of organic solar cells
- 2021 Ph.D. in Chemistry from Weizmann Institute of Science, Israel:
“Extending the Screened Range-Separated Hybrid functional approach to inorganic crystalline materials”
- 2014 M.Sc. in Nanotechnology from the Technion, Israel:
“Transparent conducting oxides as electrodes in polymer solar cells”
- 2009 B.Sc. in Physics from Caltech, USA
My research is dedicated to the development and application of a systematic hierarchy of efficient machine learning based methods for predicting reaction properties and changes in vibrational and electronic absorption spectra upon (photo-)isomerization.
Computational Chemistry • Machine Learning • Molecular Dynamics • Isomerization
Short Curriculum Vitae
| since 06/2022 | Feodor Lynen Postdoctoral Researcher, University of Luxemburg, Luxemburg
| 06/2021-05/2022 | Postdoctorate, Friedrich Schiller University Jena, Germany . Workgroup: Prof. B. Dietzek-Ivanšić
| 10/2018-06/2021 | Dissertation (Dr. rer. nat) Friedrich Schiller University Jena, Germany.Scholarship: Kekulé Fellowship. Thesis Title: Towards Operando Spectroscopy of Supramolecular Photocatalysts – A Case Study on Ru-dppz-derived Systems. Supervisor: Prof. B. Dietzek-Ivanšić. Grade: summa cum laude
| 10/2016-09/2018 | M.Sc. in Chemistry Friedrich Schiller University Jena, Germany. Thesis Title: Photoactive Proton Pumping at a Microcavity Supported Lipid Bilayer Supervisors: Prof. T. Keyes and Prof. B. Dietzek-Ivanšić (formal) Grade: 1.1 (Thesis: 1.0) |
| 03/2018-09/2018 | Research Stay, Dublin City University, Ireland. Workgroup: Prof. T. Keyes. Master Thesis (6 months) Scholarship: PROMOS DAAD |
| 10/2013-09/2016 | B. Sc. in Chemistry, Friedrich Schiller University Jena, Germany. Thesis Title: Spectroscopic Investigations of Electronic States of Copper(I)–*4H*-Imidazolate Coordination Compounds. Supervisors: Dr. Martin Schulz and Prof. B. Dietzek-Ivanšić (formal). Grade: 1.3 (Thesis: 1.0)
Jorge Alfonso Charry Martinez
- Development of multi-component molecular orbitals methods to include interparticle correlation.
- Positronic and positronium chemistry.
- Nuclear quantum effects.
- Software development of quantum chemistry packages.
2015-2017: Research assistant. Universidad Nacional de Colombia. Bogotá, Colombia. Supervisor: Professor Andrés Reyes
2012-2015: M.Sc. in Chemistry. Universidad Nacional de Colombia. Bogotá, Colombia. Thesis title "Development and implementation of an explicitly correlated Gaussian function method under the Any Particle Molecular Orbital approach, APMO" (Spanish). Supervisor: Professor Andrés Reyes
2007-2012: B.Sc. in Chemistry. Universidad Nacional de Colombia. Bogotá, Colombia. Final project title "Effect of the inclusion of nuclear-electronic correlation on the nuclear delocalization". (Spanish). Supervisor: Professor Andrés Reyes
Theoretical study of intermolecular interactions and potentials
Density functional modeling of van der Waals forces
Molecular simulations (quantum chemistry and molecular dynamics)
Theoretical chemical reaction kinetics and dynamics
Photophysics and photochemistry
Education and research:
2019- Doctoral researcher, University of Luxembourg
2018-2019 Researcher, Hungarian Academy of Sciences
2016-2018 M.Sc in Chemistry, University of Pannonia, Hungary
2013-2016 B.Sc. in Chemistry, University of Pannonia, Hungary
Gregory Cordeiro Fonseca
The core of my current work is improving Potential Energy Surface (PES) or Force Field (FF) predictions for Machine Learning (ML) models in molecular simulations. Most recently achieved using clustering techniques to reveal molecular configurations in a data set that are typically underrepresented in usual ML training methods.
2017: B. Sc. in Physics at University of Luxembourg: "Simulation of cholesteric rod-like particles".
2019: M. Sc. in Physics at University of Luxembourg: "Improving Machine Learning force fields for out-of-equilibrium geometries"
-Multiscale approaches to quantum correlations
-Many-body electronic structure theory and ab initio computations of molecular and extended systems
-Continuum variational and diffusion quantum Monte Carlo techniques
-Non-covalent interactions, fractional charge, excited states
Education and research:
2018-2019: Research Assistant, University of Ostrava, Czech Republic
2016-2019: M.Sc. in Solid State Physics, Comenius University in Bratislava, Slovakia
2013-2016: B.Sc. in Physics, Comenius University in Bratislava, Slovakia
Machine learning force fields for accurate and efficient molecular simulations.
- Scientific software development
- Computational chemistry
- Kernel machines
2020-present: Doctoral researcher, University of Luxembourg
2017-2019: MSc in Chemoinformatics and Molecular Modelling, Kazan Federal University, Russia
2013-2017: BSc in Chemistry, Kazan Federal University, Russia
- Development of universal method for the description of van der Waals interaction based on the density functional (tight-binding) theory.
- Computational materials science
- DFT modeling of solids
- Carbon nanosystems
2021 - present: Doctoral researcher, University of Luxembourg
2018 - 2020: M.Sc. [Applied Mathematics and Physics] School of Electronics, Photonics, and Molecular Physics, Moscow Institute of Physics and Technology (Russia)
2014 - 2018: B.Sc. [Applied Mathematics and Physics] Department of Molecular and Chemical Physics, Moscow Institute of Physics and Technology (Russia)
Design and validation of novel ML-based tools benchmarked on quantum mechanical reference data of chemical reactions, to be used to accurately predict components of the vast chemical reaction space emphasizing transition state features.
- 2014-2018: Bachelor in Physics at University of Florence, Italy. Thesis “Optimal estimation of parameters of complex quantum dynamics”
- 2018-2021: Master in Physics at University of Milan, Italy. Thesis ”Reinforcement learning for feedback control of continuous-variable quantum systems”
- 2020-2021: Erasmus Traineeship (for master thesis) at University of Turku, Finland
Approaching Quantum Mechanical Accuracy for Drug-Protein Binding with Machine Learning (AQMA), is a collaboration effort of the TCP group with the Luxembourg Center for Systems Biomedicine. Our aim is to combine Quantum Mechanics and Machine Learning approaches for the accurate characterization of the protein dynamics through the prediction of protein-ligand binding affinity.
2021 – present: Doctoral researcher, University of Luxembourg
2019 – 2021: M.Sc. Physics, University of Luxembourg
2015 – 2018: B.A. Physical Sciences, University of Cambridge
- Molecular dynamics (both theoretical and computational)
- Neural network models for computing thermodynamic potential
- Computational simulation of molecular systems
- Monte-carlo simulations of a molecular systems
- Transformer and attention-based neural network models
- Natural language understanding and processing
- AI-based multilingual translation and speech models
- Video and image generation models
- Muscular signitures post-mortem on rodents
2019 - 2021 : Head of Research, SRuniverse, Seoul (an AI start-up)
2017 - 2018 : Researcher, Hankook Life Science Institute, Seoul
2010 - 2011 : Student Researcher, Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa
2016 - 2017 : Master of Science, University of Tours, France
- Program : Modèles non linéaires en Physique
- Master research : Simulation of vortex behaviour in the inner-crust of a neutron star
2008 - 2014 : Bachelor of Mathematics (Hon.) , University of Waterloo, ON, Canada
- Major 1 : Mathematical Physics
- Major 2 : Pure Mathematics
- Undergraduate research : Osmotic compaction of bacterial chromosomes
Thermodynamics of simple model systems using various approaches for
treating the dispersion interaction, comparing the phenomenology of
pairwise and many-body methods.
2021-present: Doctoral researcher, University of Luxembourg
2017-2020: M.Sc. in Physics, Albert-Ludwigs-Universität Freiburg, Germany
2013-2017: B.Sc. in Physics, Albert-Ludwigs-Universität Freiburg, Germany
Machine learning methods for navigating in chemical reaction space.
Development of accurate machine learning models for large and flexible molecules.
2015 - 2021 B.Sc. and M.Sc. in Chemistry (summa cum laude), Lomonosov Moscow State University
Development of machine learning force fields to study layered halide perovskites in collaboration with the University of Mons.
- Particle physics
- Particle detector design
- Computer vision with convolutional neural network and graph neural network
2018 – 2021 M.Sc. in Weizmann Institute of Science:
"Application of deep learning for improvement of particle flow algorithm for dijet events"
2014 – 2018 BSc in applied physics. V. N. Karazin Kharkiv National University: "Exact solution of the problem of tunneling spin-polarized electrons in a magnetic field through a quantum dot"
Raul Ian Sosa
Build computationally efficient multi-scale models that incorporate a quantum many-body treatment of van der Waals, and define continuum mechanics properties arising from quantum mechanics.
- Continuum modeling of large ensembles of coupled non-linear systems.
- High efficiency computing and GPU acceleration trough CUDA.
- End-to-end voice conversion/speech generation using attention-based NN models.
- Data augmentation for speech recognition and image classification models.
2022-present: Doctoral researcher, University of Luxembourg
2019-2021: M.Sc. in Physics, Instituto Balseiro, Argentina
2017-2019: B.Sc. in Physics, Instituto Balseiro (combined degree with Universidad de Buenos Aires), Argentina
2015-2017: B.Sc. in Physics, Universidad de Buenos Aires (combined degree with Instituto Balseiro), Argentina
Research topics: Predict with machine learning algorithm forces & energies for complex biomolecular systems learning from DFT calculations. Positive results would be matching a relatively good accuracy and a competitive computational time regarding the calculation methods.
-Master In Silico Drug Design: double degree master:
-Strasbourg University (6months)
-Degli Studi Di Milano (6months)
-Paris Diderot (6months)
-Internship in UHA Mulhouse (6months)
My current research focuses on the understanding of nuclear quantum effects in inorganic and organic molecules and clusters using machine learned force fields. We use two machine learning methodologies for the force fiend reconstruction process: 1) symmetric gradient domain machine learning (sGDML), and 2) continuous-filter convolutional neural network (SchNet). Specifically, the ongoing projects are:
- Development of new machine learning models to describe large molecules.
- Understanding the dynamical implications of local and non-local electronic interactions (e.g. H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion and n→π∗transitions).
- Nuclear quantum effects in inorganic clusters.
- Vibrational and thermodynamical properties of molecules, clusters and nanoparticles
- Vector Valued Kernel Ridge Regression
- Contributor to the sGDML package (http://quantum-machine.org/gdml/doc/index.html)
2012 - 2016: PhD in Materials Science (Hons.) form Universidad Nacional Autónoma de México, Mexico.
2009 - 2011: Master in Science (Physics) (Hons.) from Universidad Nacional Autónoma de México, Mexico.
2003 - 2008: Bachelor in Physics (Hons.) from Universidad Autónoma de Sinaloa, Mexico.
Enrolled in a PhD in Economics, my current research focuses on applying Machine Learning algorithms to Economic data to predict and interpret the determinants of multiple Economic phenomena, including individual well-being and self-assessed life satisfaction.
2011 - 2014: Bachelor of Science (BSc), Economics and Management, Bocconi University
2014 - 2017: Master of Science (MSc), Economic and Social Sciences, Bocconi University Final year thesis: "P - value and its frequentist misinterpretation: a case study in a Bayesian framework".
Sep. 2015 - Jan. 2016: Research Assistant intern, lavoce.info
Jun 2017 - Nov. 2017: EU Inbound Analytics intern, Amazon.com