CYGNUS Models for Spectral Energy Distributions of Galaxies

by Prof. Andreas Efstathiou, European University Cyprus

Date: Tuesday, 20 April 2021


Astrophysics concerns the study of the sequence of events that led to the formation of galaxies and the supermassive black holes that usually reside at their centres. Understanding of the complex astrophysics that led to the observed distribution of galaxies, both in terms of properties and numbers, is usually sought within the framework of the standard ΛCDM (Lambda Cold Dark Matter) cosmological model.

Radiative transfer models for galaxies and active galactic nuclei (AGN) are now routinely used for interpreting multi-wavelength observations of galaxies and are increasingly being incorporated into models of galaxy formation and evolution. CYGNUS (CYprus models for Galaxies and their NUclear Spectra) is a collection of radiative transfer models for AGN tori, starburst galaxies and host galaxies.

In this talk, ongoing work funded by the European Space Agency under the PECS program and making use of the CYCLONE facility will be described. The project aims to develop further the CYGNUS collection of models, develop a new method for fitting them to data using a Markov Chain Monte Carlo (MCMC) code and test the method with a large sample of galaxies with excellent photometry and infrared spectrophotometry from the Spitzer Space Telescope. Numerous recent applications of the CYGNUS models as well as prospects for applications with forthcoming missions such as the James Webb Space Telescope (due for launch in late 2021) will be discussed.



Adaptive Algorithms for Schrodinger Type Equations

by Prof. Theodoros Katsaounis, University of Crete, Greece

Date: Tuesday, 13 April 2021


In this talk, Prof. Katsaounis will present a series of results concerning the development and implementation of space time adaptive algorithms for obtaining numerical solutions to linear and nonlinear Schrodinger type of equations. The mathematical foundations of the algorithms are rigorous aposteriori estimates developed for a Crank-Nicolson finite element method for these models. This is a joint work with I.Kyza.



Designing Novel Nanoporous Materials for Applications in Energy and Environment

by Prof. George E. Froudakis, Department of Chemistry, University of Crete, Greece

Date: Tuesday, 06 April 2021


Machine learning techniques (ML) are powerful tools already used in science and industry since their computational cost is by several orders of magnitude lower than that of the “conventional” approaches. However, their ability to provide accurate predictions strongly depends on the correct identification of those parameters (descriptors) that will allow the algorithm to effectively learn from past data. Other critical factors that affect the quality of the predictions are the size and the quality of the dataset used for the training of the algorithm as well as the correct estimation of the training size.
Aiming at both, the transferability of our model and the reduction of the training data set, we introduce 2 different classes of descriptors, based on fundamental chemical and physical properties: Atom Types and Atom Probes. The main difference from previous models is that our descriptors are based on the chemical character of the atoms which consist of the skeleton of the materials and not their general structural characteristics. With this bottom up approach we go one step down in the size of the descriptors employing chemical intuition.

In parallel, an automatic procedure of identifying the appropriate size of the training set for a given accuracy was developed. A novel training algorithm based on “Self-Consistency” (SC) replaced the standard procedure of linearly increasing of the training set. Our SC-ML methodology was tested in 5.000 experimentally made MOFs for investigating the storage of various gases (H2, CH4, CO2, H2S, H2O). For all gases examined, the SC-ML methodology leads to significantly more accurate predictions, while the number of MOFs needed for the training of the ML algorithm in order to achieve a specified accuracy can be reduced by an order of magnitude. In addition, the universality and transferability of our ML model was proved by predicting the gas adsorption properties of a different family of materials (COFs) after training of the ML algorithm in MOFs.

We propose a new methodology for the construction of artificial data (artificial MOFs) with the desired properties that will be used for ML training. This will enable ML algorithms for achieving improved predictions, in particular for high-performing materials. We demonstrate that, after using the artificial data, the capability of the ML algorithms to classify new top-performing MOFs as such, improves remarkably. We are also confident that the present methodology represents an important contribution toward the development of predictive models aiming to the discovery of new materials with outstanding properties. In addition, the main idea of this approach, can be used in many other applications of ML methodologies for overcoming the inherent problem of extrapolation.


Structural Determinants of Function in Complex Microvascular Tissues

by Igor Chernyavsky, Department of Mathematics; Maternal & Fetal Health Research Centre, University of Manchester, UK

Date: Tuesday, 23 March 2021


Multi-scale structural assessment of biological tissues and organs is essential to gain insight into their structure-function relationship [1,2]. For example, the human placenta is a highly complex and unique multi-functional organ. It is a life-support system that not only nourishes a growing fetus, but also determines her or his life-long health. The placental primary exchange units, terminal villi, host dense networks of fetal capillaries and are interfaced with maternal blood, percolating a disordered porous medium. While placental transport at the micro-scale can be described by established models, systematically upscaling the transport and quantifying the associated uncertainty at the organ-level remain open challenges [1]. This talk will summarise recent progress in advanced 3D microscopy and its assimilation into mathematical models that predict placental function [2]. The models reveal a surprising role of microstructure in the upscaled predictions and demonstrate certain universality of reduced-order approximations for a wide class of solutes and transport regimes [3]. The developed approaches could also be useful for multi-scale modelling of solute exchange in other complex microvascular systems.


Synergy of HPC and Nuclear Physics to Resolve Long-standing Puzzles

by Martha Constantinou, Assist. Prof., University of Temple in Philadelphia, USA

Date: Tuesday, 16 March 2021


More than 99% of the mass of the visible matter resides in hadrons which are bound states of quarks and gluons, collectively called partons. These are the fundamental constituents of Quantum Chromodynamics (QCD), the theory of strong interactions. While QCD is a very elegant theory, it is highly non-linear and cannot be solved analytically, posing severe limitations on our knowledge of the structure of the hadrons. Lattice QCD is a powerful first-principle formulation that enables the study of hadrons numerically, which is done by defining the continuous equations on a discrete Euclidean four-dimensional lattice.

Hadron structure is among the frontiers of Nuclear and Particle Physics, with the 2015 Nuclear Science Advisory Committee’s Long Range Plan for Nuclear Physics identifying a future electron-ion collider (EIC) as the highest priority for new facility construction. Last year, the National Academies of Sciences, Engineering, and Medicine (NAS) released an assessment report which strongly endorses the science case for an EIC. The NAS report identified three high-priority science questions to understand hadron structure:

1. How does the mass of the nucleon arise?
2. How does the spin of the nucleon arise?
3. What are the emergent properties of dense systems of gluons?



Active (Non-) Particles: Donuts, Curved Rods, and Flexibility

by Dr. Thomas Montenegro-Johnson, Senior Lecturer in Mathematical Biology, University of Birmingham

Date: Tuesday, 9 March 2021


Fun things start to happen when active particles get kinky. This talk will focus on what happens when active particles move from point-like geometries such as spheres, into non-trivial topologies such as tori, and slender 3D curved structures, offering opportunities for rich dynamical behaviours and new control strategies exploiting shape changes and flexibility.



Special Displacement Method for the Calculation of Materials’ Properties at Finite Temperatures

by Marios Zacharias, Post-doctoral Researcher, Department of Mechanical and Materials Science Engineering, Cyprus University of Technology

Date: Tuesday, 16 February 2021


Typical first-principles (parameter free) calculations of the optoelectronic properties of solids are performed by describing the nuclei as classical particles clamped at their crystallographic positions. This approximation undermines the accuracy of predicting materials properties at, or above, room temperature, since it misses quantum nuclear and thermal effects [1]. Recently, an ex-novo approach, namely the special displacement method (SDM) [2,3], has been developed to include these effects in state-of-the-art electronic-structure calculations.

In this talk, the speaker will demonstrate how high-performance computing in conjunction with SDM can advance the field of electronic-structure calculations to explore novel emergent phenomena arising from the interplay of electron-phonon interactions. He will present some applications of SDM, including the calculation of full temperature-dependent band structures and optical spectra of crystalline and nano-crystalline quantum systems [2,3,4]. SDM holds promise for high-throughput calculations of many material’s properties at finite temperature.



Multiscale Modeling of Biomolecules and Materials

by Zoe Cournia, Associate Professor of the Biomedical Research Foundation, Academy of Athens

Date: Tuesday, 02 February 2021


In this webinar, the method development and applications of multiscale computational techniques for the modeling of materials and biomolecules ranging from atomistic and coarse-grained Molecular Dynamics (MD) simulations, Monte Carlo, Markov state models, and Machine Learning with applications in drug design and drug delivery design will be discussed. Biological membranes comprise fascinating examples of soft matter interfaces involved in a wide range of biological and industrial functions. Several aspects of the structure and dynamics of biomembranes as well as methodologies for targeting specific membrane interfaces for developing novel drug candidates and nanoparticle delivery systems will be presented.



Computer Simulation Aspects of Nanoparticle and Nanodevice Design 

by Staff Scientist, "Nanoparticles by Design” Unit leader at OIST Graduate University, Japan, Visiting Assistant Professor at the Particle Technology Laboratory at ETH Zürich, Prof. Panagiotis Grammatikopoulos

Date: Tuesday, 26 January 2021


Cluster beam deposition (CBD) is a term that collectively describes various physical methods of nanoparticle synthesis by nucleation and growth from a supersaturated atomic vapour. It provides a solvent- and effluent-free method to design monodisperse multifunctional nanoparticles with tailored characteristics that can be subsequently deposited on a desired substrate or device in the soft-landing regime under ultra-high vacuum.

In this talk, I will explain the main mechanisms that control the basic properties of individual nanoparticles such as size, shape, or chemical ordering, based on various setups of CBD sources. Moving to a coarser scale, I will bring up examples where larger structures can be designed using nanoparticles as their functional building blocks, such as novel sensors and energy storage devices. To date, CBD faces two main limitations that need to be overcome for real-world applications: (i) limited yield, and (ii) precise structural control. The main thesis of this talk is that both challenges can be tackled by in-depth theoretical understanding of both the thermodynamics and kinetics of nucleation & growth. To this end, atomistic computer modelling can be an invaluable tool, complementing experimental fabrication and guiding future source design.



High Performance Computing in Structural Biology
by Assistant Prof. Daskalakis Vangelis, leader of the Computational Environmental Modeling (CEM) Group, Department of Environmental Science and Technology (EST) at the Cyprus University of Technology (CUT)

Date: Tuesday, 19 January 2021


Structural biology is a branch of molecular biology, biochemistry, and biophysics with the focus on the molecular structure of biological macromolecules (like proteins and membranes). A key question of Structural Biology is how these macromolecules acquire their structures. A combination of methodologies like Molecular Dynamics and Markov state modeling that is supported by High Performance Computing (HPC) leads to an ideal description of macromolecule conformations and dynamics at all-atom resolution. The latter is a valuable information in Structural Biology. In this seminar two key HPC projects will be presented that relate to: (A) the out-break of the novel coronavirus (SARS-CoV-2) that causes the respiratory tract disease COVID-19 and the dynaics of two key viral proteins that can potentially be used as drug-targets or source of epitope vaccines, and (B) the delicate balance between light harvesting and photoprotective modes of the Light Harvesting Complexes of Photosystem II, that can potentially be used as models for artificial photosynthesis.



Fostering innovation with HPC, advanced simulations and AI and Big Data
by Christos Christodoulou, Managing Coordinator - Innovation Scout, SimEA, CaSToRC, The Cyprus Institute

Date: Tuesday, 12 January 2021


The computation-based science and technology research center (CaSToRC) has recently become a National Competence Center in HPC and the host of the prestigious SimEA ERA Chair project that aims to expand the research activities of the center and forge innovative collaborations with industry.

In this talk, Christos, the Innovation Scout of SimEA, will talk about the Management and Innovation Office (MIO) established at CaSToRC, as a structural change aiming to promote the innovation strategy of the center. He will also talk about the core competences of the SimEA team and of CaSToRC, and how the industrial and governmental sector can use HPC to achieve breakthroughs in areas like energy and environment, design of novel materials, digitalization of industry, circular economy, oil and gas production, medicine, and financial risk assessment.




Presentations by our PhD students who have recently joined the SimEA research team. Here, they present their MSc research projects and give an overview of the research that they will be conducting during their PhD at The Cyprus Institute.

Date: Tuesday, 8 December 2020


The Optimization Algorithm in Machine Learning
by Eleftherios Christofi, PhD Student, SimEA project, The Cyprus Institute In this weminar


The last two decades have marked a rapid and significant growth of the Artificial Intelligence field. Deep learning using artificial neural networks became an essential tool for a vast number of applications fields. The structure of deep learning relies on basic concepts from several mathematical fields, such as linear algebra, calculus, optimization, statistics. This presentation is an introduction to the mathematical background of deep learning. In particular, we focus on the optimization algorithm widely used, namely the stochastic gradient descent. We study and compare the behaviour of different variants of this algorithm under various circumstances and summarize their strengths and weaknesses. The goal of this presentation is to provide the viewer with the knowledge to comprehend the training procedure of a neural network.


Development of an algorithm and software for computing Pareto fronts with a gradient-based method, with applications in aerodynamics
by Nikolaos Patsalides, PhD Student, SimEA project, The Cyprus Institute


This diploma thesis proposes, develops and evaluates a low-cost algorithm and software for computing Pareto fronts with a gradient-based method, i.e. a method that uses the gradient of the cost functions, by implementing the algorithm in aerodynamic shape optimization. It is based on a method that successively computes points on the front, by properly forcing all objectives to equality constraints, apart from one that becomes the target to be minimized. After the description of the algorithm's steps, applications in aerodynamic shape optimization of an airfoil are presented. Two- and three-objective problems are solved, with bounded design variables and equality or inequality constraints.



Webinar: Bottom-up Approach - From Model Molecular Systems to Complex Polymer Materials
by Dr. Petra Bacova, Post-doctoral researcher, SimEA project, The Cyprus Institute

Date: Tuesday, 24 November 2020


Following the lecture of Prof. Harmandaris, a postdoctoral SimEA fellow Dr. Petra Bacova will shed further light on the computational design of advanced materials. The main focus will be on the complex polymer materials, addressing three main questions: WHY these materials are interesting, WHAT their composition is and HOW we can model their properties computationally.



Commencement of seminar series - inaugural lecture by the ERA chair Prof V. Harmandaris

Date: 5 November 2020

Title: Computational Science and Engineering of Complex Materials: The SimEA ERA Chair Initiative


The ERA Chair project “Modeling and Simulation for Engineering Applications” (SimEA), funded by EU, refers to a new initiative at CaSToRC of CyI, regarding the challenges and opportunities arising when advanced computing and data science are utilised to solve engineering problems. The research team, led by ERA Chair Prof. V. Harmandaris, will work on the development of mathematical and computational methodologies for complex molecular systems, with important applications in nano/bio technology.

The team will pursue a program of research excellence and innovation by applying and developing mathematical and computational methods, including multiscale modeling, physics-based and/or data-driven molecular models, uncertainty quantification, and machine learning methods, integrated with High-Performance Computing, for tackling challenging problems in different application areas related to Computational Science and Engineering.  Examples include a broad range of systems/materials of great scientific and technological interest, such as nanocomposites, polymers, graphene-based nanostructured materials, proteins, and biomolecular systems.

In this inaugural seminar of the seminar series under the SimEA, we will give an overview of the main objectives and research topics of the SimEA. Our vision is to create a regional and national hub for advanced computing and its applications at CyI, collaborating with academia and the private sector, across several research areas, such as: (a) Multi-scale Modelling and Simulations of Nanostructured Materials, (b) Data-driven Machine Learning Approaches for Modelling Across Scales, (c) Modelling of Biomolecular Systems for Biotechnology Applications, (d) Statistical Inference for Uncertainty Quantification and Model Selection.