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 imaginary time path integral methods to calculate quantum partition functions.
- Contribution of the continuum electronic states to the van der Waals interactions.
- Development of numerical methods to study the geometric phase effect in the dynamics of molecular systems.
- Kinetics and dynamics of elementary reactions
- Semiclassical and quantum dynamics of molecular collisions
- Collision induced spectroscopy (collision induced absorption and emission)
- Ab inito quantum chemistry (wave function based methods)
- Development of global potential energy and dipole surfaces of reactions
- DFT and TD-DFT characterization of organometallic molecules
- Experimental photophysics and photochemistry of light harvesting molecules and nanomaterials
2006 - 2011 – M.Sc. in Chemistry, University of Pannonia, Hungary
2011 - 2015 – Ph.D. in Theoretical Chemistry, University of Pannonia, Hungary
2015 - 2017 – Research Assistant, University of Pannonia, Hungary
2017 - 2019 – Postdoc, University of Luleå, Sweden,
- 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”
I am currently working with researchers in the economics department to investigate how machine learning methods can be used with socio-economic datasets.
During my PhD, I worked on force field parameterization and method development for small molecules and proteins. My previous work as a postdoctoral researcher involved parametrizing permutationally invariant polynomials for small molecules.
2011-2014 BSc, Imperial College London
2014-2015 MPhil, University of Cambridge
2015-2018 PhD, University of Cambridge - Quantum Mechanically Derived Biomolecular Force Fields
2018-2019, Research Associate, University of Cambridge
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
- Machine learning approach to understand chemical space
- Non-adiabatic Molecular Dynamics
- van der Waals interaction in large molecular systems
Expertise in Theoretical Models and Methods:
- Carrier Transport formalism based on Non-adiabatic Molecular Dynamics & Boltzmann Transport Equation
- Density Functional Theory (DFT) & Density Functional Perturbation Theory (DFPT)
- Conceptual Density Functional Theory based reactivity descriptors (Local, Non-local and Global)
- Time-Dependent Density Functional Theory (TDDFT)
- Data-driven materials design & analysis
- Classical, ab-initio and Machine Learning molecular dynamics simulations
Aug 2021 - Present: Postdoctoral Associate, University of Luxembourg, Luxembourg
Mar, 2019 - Jul, 2021: Postdoc, University of California San Diego, USA
Jul, 2017 - Feb, 2019: Postdoctoral Research Fellow & Affiliate, Lawrence Berkeley National Laboratory, USA, Shenzhen University, China
Jan, 2017 - Jun, 2017: Research Associate, Jawaharlal Nehru Center For Advanced Scientific Research, Bangalore, India
Nov, 2016 - Dec, 2016: Visiting Scientist, Institute of Semiconductors, Chinese Academy of Science, Beijing, China
2011-2017: PhD, Jawaharlal Nehru Center For Advanced Scientific Research, Bangalore, India
2009-2011: Master of Science, Department of Chemistry, Indian Institute of Technology Kharagpur, India
Bachelor of Science, Subject: Chemistry(Hons), Physics, Mathematics, The University of Burdwan, India
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
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.
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”.
- 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
My work focuses on the modelization of the geometric phase in molecular systems through a Feynman Path Integral approach. In particular, we aim at the construction of efficient numerical algorithms for the evaluation of the Molecular Aharonov-Bohm Effect and its emergence in real systems.
2018-2018: Stage at Institute Néel, Grenoble, under the supervision of Simone Fratini and Arnaud Ralko.
2017-2018: Erasmus+ mobility period at Grenoble INP, Grenoble.
2016-2018: Master Degree in Mathematics, Verona.
2013-2016: Bachelor Degree in Applied Mathematics, Verona.
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
Expertise and interests:
Kernel methods in machine learning
Chemical software development
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"
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