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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.

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Research Interests: Statistical physics, imaginary-time path integral methods, nuclear quantum effects, ab initio simulations

Working Experience:

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:

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.

Expertise:

Computer simulation, statistical mechanics, thermodynamics, kinetics, rare event statistics and machine learning.

History:

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.

Cross-Affiliations: Centre for complex living systems https://cls.uni.lu/

Scholar: https://scholar.google.com/citations?user=quu2fm8AAAAJ&hl=en

University staff directory: https://wwwen.uni.lu/research/fstm/dphyms/people/josh_berryman

Personal Homepage: http://berrymanscience.com

Github:  https://github.com/tojb

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Education:

-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.

Research interests:

- 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.

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Current interests:
- 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

Expertise:

- Quantum Monte Carlo methods
- Density functional theory
- Ab initio wave function based quantum chemistry methods

- Strongly correlated systems.

Academic history:

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

Current interests

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.

Expertise

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.

Education

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

Background:

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)

Current interests:

-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.

Expertise:

-String Theory

-Supersymmetric quantum field theories in various dimensions

-Conformal Field Theories in two dimensions

Expertise

- 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

Education

[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.

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Current interests:

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.

Expertise:

- 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.

Professional Experience:

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.

Education:

2014-2019: PhD, EPFL, Switzerland.

2009-2014: Diploma of Chemical Engineering, National Technical University of Athens, Greece.

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Current interests:

  - 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

Expertise:

  - Density functional theory (DFT)

  - Time-dependent density functional theory (TDDFT)

  - Device physics of organic solar cells

Education:

  - 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

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B.Sc.:  Jadavpur University, Kolkata, India.

M.Sc.: Indian Institute of Technology (IIT), Kanpur, India.

Ph.D.: Emory University, Atlanta, U.S.A. (Supervisor: Prof. Joel M. Bowman)

Expertise:  Theoretical Chemistry, Reaction Dynamics, Machine-Learning, Electronic  Structure Theory, Molecular Dynamics, Nuclear Quantum Effects

Current  Interest: Development of reliable and efficient Machine-Learning models  for atomistic simulations of solar energy materials

Bachelor of Science in Physics, LADY BRABOURNE COLLEGE, CALCUTTA UNIVERSITY, India

Master of Science in Physics,VELLORE INSTITUTE OF TECHNOLOGY, India

PhD in Chemistry ,INSTITUTE OF PHYSICAL CHEMISTRY,POLISH ACADEMY OF SCIENCES, Poland

Expertise:  Non-linear dynamics, Machine-Learning, Chemical computing

Current research interests: Using quantum mechanical calculations with  DFTB to identify the most suitable descriptors for training machine  learning models. These models  are trained to predict important ADMET  properties of molecules, which are crucial in drug design. In other  words, I am exploring ways to use machine learning to make drug  discovery faster and more effective

Keywords: DFTB calculations, Computational Chemistry, Drug Design, Machine learning

History

2017 - 2022, Postdoctoral researcher,  Institute of Microbiology of the Academy of Sciences, Nové Hrady, Czechia.

2014 - 2016, Postdoctoral researcher, Institute of Organic Chemistry and Biochemistry, Prague, Czechia.

2010 - 2013, Phd student, University of Lorraine, Nancy, France.

Expertise

_ Molecular dynamics simulations of biomolecules

_ Free-energy calculations

_ Molecular modelling (Homology modelling, docking, …)

Research interests:

_ Phospholipidic membranes and transmembrane proteins

_ Biomolecules

_ Protein assembly

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Research interests:

- Development of multi-component molecular orbitals methods to include interparticle correlation. 

- Positronic and positronium chemistry. 

- Nuclear quantum effects.

- Software development of quantum chemistry packages. 

Education:

2019-2023: Doctoral Researcher. University of Luxembourg

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

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Current interests:

             Theoretical study of intermolecular interactions and potentials
             Density functional modeling of van der Waals forces

Expertise:  

             Molecular simulations (quantum chemistry and molecular dynamics)
             Theoretical chemical reaction kinetics and dynamics
             Photophysics and photochemistry
             Computational chemistry

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

Interest:

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.

Education:

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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"

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Current interest:
-Multiscale approaches to quantum correlations

Expertise:
-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

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Research interests:

I am interested in studying quantum vacuum effects using the  fascinating theory of quantum electrodynamics and quantum field theory.  Currently I am focused on Casimir and Casimir-Polder interactions,  self-energies and vacuum polarization.

Education:

2017-2022: PhD in Physics, University of Luxembourg, Luxembourg

2009-2012: MSc in Physics, Graduate University of Advanced Technology, Kerman-Iran

2005-2009: BSc in Physics, Department, University of Kerman, Kerman-Iran

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Current Interest:

- Understanding the role of long-range van der Waals interactions in large biomolecules

- Developing Machine Learning Force Fields (MLFFs) for accurate depiction of diverse Biomolecular Dynamics

Expertise:

- Density Functional Theory (DFT), Electronic Structure Theory

- Catalysis, Reaction Mechanism and Kinetics, Non-covalent interactions

- Biomolecular dynamics of toxic nerve agent hydrolysis

Education:

2024: Postdoctoral researcher, University of Luxembourg, Luxembourg

2023: Postdoctoral researcher, Bar-Ilan University, Israel

2022: Ph.D. in Computational Chemistry, Ben-Gurion University, Israel

2017: M.Sc. in Chemistry, North-Eastern Hill University, India

2014: B.Sc. in Chemistry, Dibrugarh University, India

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Current Interests:

- Modelling excited-state properties of molecules and materials using quantum chemistry methods

- Predicting excited-state properties with machine learning algorithms

Expertise:

- Time-Dependent Density Functional Theory (TD-DFT)

- Many-Body Green's Function (GW) and Bethe-Salpeter Equation (BSE) formalisms

- Coupled-Cluster methods with Linear Response (LR) and Equation-of-Motion (EOM) formalisms

- Modelling of vibronically-resolved absorption and emission spectra

Education:

2024 - present         Postdoctoral researcher

University of Luxembourg, Luxembourg

2021 - 2024               PhD in Theoretical Chemistry

Nantes Université, France

2019 - 2021               Erasmus Mundus Joint Master Program in Chemical NanoEngineering (EMJM CNE)

Aix-Marseille University, France

Wroclaw University of Science and Technology, Poland

Tor Vergata University of Rome, Italy

2013 - 2018                Bachelor's and Master's degrees in Chemical Technology and Engineering

National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

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Current interests:
- Development of universal method for the description of van der Waals  interaction based on the density functional (tight-binding) theory.

Previous experience:

- Computational materials science

- DFT modeling of solids

- Carbon nanosystems

Education:
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)

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Current Project:

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.

Education:

-  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

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Current research:

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.

Education:

2021 – present: Doctoral researcher, University of Luxembourg

2019 – 2021: M.Sc. Physics, University of Luxembourg

2015 – 2018: B.A. Physical Sciences, University of Cambridge

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Homepage

Current Research:
- Molecular dynamics (both theoretical and computational)
- Neural network models for computing thermodynamic potential
- Computational simulation of molecular systems

Expertise:
- 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

Professional Experience:
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

Education:
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

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Current research:

Machine learning methods for navigating in chemical reaction space.

Development of accurate machine learning models for large and flexible molecules.

Education:

2015 - 2021 B.Sc. and M.Sc. in Chemistry (summa cum laude), Lomonosov Moscow State University

Current Interests:
Development of machine learning force fields to study layered halide perovskites in collaboration with the University of Mons.

Previous experience:
 - Particle physics
 - Particle detector design
 - Computer vision with convolutional neural network and graph neural network

 Education:
 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"

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Current Research
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.

Experience
- 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.

Education
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.

Last positions:

-Master In Silico Drug Design: double degree master:

-Strasbourg University (6months)

-Degli Studi Di Milano (6months)

-Paris Diderot (6months)

-Internship in UHA Mulhouse (6months)

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Research  Interests: PT Symmetry, Non-Hermitian Quantum Mechanics, Path-Integral  Methods, General Relativity, Gravitational Waves, Black hole scattering

Jun 2022 - November 2022 Honorarium (Remote research consultant) AEI, Hannover, Germany

Oct-Dec 2021 Visiting Scholar at Institut de Mathématiques de Bourgogne, University of Burgundy Franche-Comté, Dijon

2020-2021 Research Intern, AEI, Hannover

2015–2017 Master of Science (Physics), National Institute of Technology, Rourkela, India.

2011–2015 Bachelor of Science (Physics), National Institute of Technology, Rourkela, India

Research Topics

Machine-learned quantum Force Fields for molecule-protein interactions.

Education

2023-present: Doctoral researcher, University of Luxembourg

2020-2022:  M.Sc. in Physics of Complex Systems, University of Torino, Italy: “A  Hopfield-like algorithm for cell type classification”

2017-2020: B.Sc. in Physics, University of Torino, Italy: “Study of the critical behavior of the 2-D Ising model”

Current research: 

Investigation the process of biomolecule  information transfer, with a specific emphasis on allostery. The  application and validation of machine learning force fields (MLFF) is  also part of the research. These new methods will enable the in-depth  examination of long-range non-covalent forces critical to information  transfer.

Previous Positions:

- Research  assistant, GPCR Drug Discovery group of GRIB, Instituto Hospital del  Mar de Investigaciones Médicas (IMIM), Barcelona, Spain (2022-2023)

Education:

- MSc in Bioinformatics for Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain (2021-2023)

- BSc in Biotechnology, Universidad de Oviedo, Oviedo, Spain (2017-2021)

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Current Interests:

• Development of machine learning force fields with balanced description of short- and long-range interactions for large systems

• Combination of machine learning force field and electronic structure frameworks

Expertise:

• Density Functional Theory

• Molecular Dynamics

• Machine Learning

Education:

• Since 2023 Doctoral researcher, University of Luxembourg

• 2023 M.Sc. in Chemistry, Saarland University, Germany

• 2019-2020 R&D Intern tesa SE, Hamburg, Germany

• 2019 B.Sc. in Chemistry, Bielefeld University, Germany