arXiv subjects

Georgios A. Tritsaris

Publications and source records attributed to Georgios A. Tritsaris.

8 recordsLinked to original sources

arXiv2025

Occupational Tasks, Automation, and Economic Growth: A Modeling and Simulation Approach

The Fourth Industrial Revolution commonly refers to the accelerating technological transformation that has been taking place in the 21st century. Economic growth theories which treat the accumulation of knowledge and its effect on production endogenously remain relevant, yet they have been evolving to explain how the current wave of advancements in automation and artificial intelligence (AI) technology will affect productivity and different occupations. The work contributes to current economic discourse by developing an analytical task-based framework that endogenously integrates knowledge accumulation with frictions that describe technological lock-in and the burden of knowledge generation and validation. The interaction between production (or automation) and growth (or knowledge accumulation) is also described explicitly. To study how automation and AI shape economic outcomes, I rely on high-throughput calculations of the developed model. The effect of the model's structural parameters on key variables such as the production output, wages, and labor shares of output is quantified, and possible intervention strategies are briefly discussed. An important result is that wages and labor shares are not directly linked, instead they can be influenced independently through distinct policy levers. Generally, labor share depends sensitively on capital-labor ratio, while wages respond positively to larger knowledge stocks.

econ.TH↗

arXiv2022

Machine Learning for the edge energies of high symmetry Au nanoparticles

We present data-driven simulations for gold nanostructures, and develop a model that links total energy to geometrical features of the particle, with the ultimate goal of deriving reliable edge energies of gold. Assuming that the total energy can be decomposed into contributions from the bulk, surfaces, edges, and vertices, we use machine learning for reliable multi-variant fits of the associated coefficients. The proposed method of total energy calculations using machine learning produces almost ab-initio-like accuracy with minimal computational cost. Furthermore, a clear definition and metric for edge energy is introduced for edge-energy density calculations that avoid the troublesome definition of edge length in nanostructures. Our results for edge-energy density are 0.22 eV/Å for (100)/(100) edges and 0.20 eV/Å for (111)/(111) edges. Calculated vertex energies are about 1 eV/atom. The present method can be readily extended to other metals and edge orientations as well as arbitrary nanoparticle shapes.

cond-mat.mtrl-sci↗

arXiv2021

Computational Design of Moiré Assemblies Aided by Artificial Intelligence

Two-dimensional (2D) layered materials, demonstrating significantly different properties from their bulk counterparts, offer a materials platform with potential applications from energy to information processing devices. Although some single- and few-layer forms of materials such as graphene and transition metal dichalcogenides have been realized and thoroughly studied, the space of arbitrarily layered assemblies is still mostly unexplored. The main goal of this work is to demonstrate precise control of layered materials' electronic properties through careful choice of the constituent layers, their stacking, and relative orientation. Physics-based and AI-driven approaches for the automated planning, execution, and analysis of electronic structure calculations are applied to layered assemblies based on prototype one-dimensional (1D) materials and realistic 2D materials. We find it is possible to routinely generate moiré band structures in 1D with desired electronic characteristics such as a band gap of any value within a large range, even with few layers and materials (here, four and six, respectively). We argue that this tunability extends to 2D materials by showing the essential physical ingredients are already evident in calculations of two-layer MoS$_2$ and multi-layer graphene moiré assemblies.

cond-mat.mes-hall↗

arXiv2020

LAN -- A materials notation for 2D layered assemblies

Two-dimensional (2D) layered materials offer intriguing possibilities for novel physics and applications. Before any attempt at exploring the materials space in a systematic fashion, or combining insights from theory, computation and experiment, a formal description of information about an assembly of arbitrary composition is required. Here, we introduce a domain-generic notation that is used to describe the space of 2D layered materials from monolayers to twisted assemblies of arbitrary composition, existent or not-yet-fabricated. The notation corresponds to a theoretical materials concept of stepwise assembly of layered structures using a sequence of rotation, vertical stacking, and other operations on individual 2D layers. Its scope is demonstrated with a number of example structures using common single-layer materials as building blocks. This work overall aims to contribute to the systematic codification, capture and transfer of materials knowledge in the area of 2D layered materials.

cond-mat.mtrl-sci↗

arXiv2020

Electronic structure calculations of twisted multi-layer graphene superlattices

Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and studied, a systematic investigation of the properties of arbitrarily layered graphene assemblies is still lacking. We introduce theoretical concepts and methods for the processing of materials information, and as a case study, apply them to investigate the electronic structure of multi-layer graphene-based assemblies in a high-throughput fashion. We provide a critical discussion of patterns and trends in tight binding band structures and we identify specific layered assemblies using low-dispersion electronic bands as indicators of potentially interesting physics like strongly correlated behavior. A combination of data-driven models for visualization and prediction is used to intelligently explore the materials space. This work more generally aims to increase confidence in the combined use of physics-based and data-driven modeling for the systematic refinement of knowledge about 2D layered materials, with implications for the development of novel quantum devices.

cond-mat.mes-hall↗

arXiv2019

Effects of Lithium Intercalation in Twisted Bilayer Graphene

We investigate the effects of lithium intercalation in twisted bilayers of graphene, using first-principles electronic structure calculations. To model this system we employ commensurate supercells that correspond to twist angles of 7.34$^\circ$ and 2.45$^\circ$. From the energetics of lithium absorption we demonstrate that for low Li concentration the intercalants cluster in the AA regions with double the density of a uniform distribution. The charge donated by the Li atoms to the graphene layers results in modifications to the band structure that can be qualitatively captured using a continuum model with modified interlayer couplings in a region of parameter space that has yet to be explored either experimentally or theoretically. Thus, the combination of intercalation and twisted layers simultaneously provides the means for spatial control over material properties and an additional knob with which to tune moiré physics in twisted bilayers of graphene, with potential applications ranging from energy storage and conversion to quantum information.

cond-mat.mes-hall↗

arXiv2019

Interdisciplinary collaboration in research networks: Empirical analysis of energy-related research in Greece

Technological innovation is intimately related to knowledge creation and recombination. In this work we introduce a combined statistical and network-based approach to study collaboration in scientific authorship. We apply it to characterize recent research efforts in renewable energy technology and its intersections with the domains of nanoscience and nanotechnology with focus on materials, and electrical engineering and computer science in Greece and its broader European and international environment as a case study. Using our methods we attempt to illuminate the processes which underlie knowledge creation and diversification in these research networks: a (positive) relationship between expenditure on research and development and the extent and diversity of team-based research at the intersections of the three domains is established. Our specific findings collectively provide insights into the collaboration structure and evolution of energy-related research activity in Greece, while our methodology can be used for evidence-based design, monitoring, and evaluation of interdisciplinary research programs.

cs.DL↗

arXiv2015

\textit{Ab-initio} Tight-Binding Hamiltonian for Transition Metal Dichalcogenides

We present an accurate \textit{ab-initio} tight-binding hamiltonian for the transition-metal dichalcogenides, MoS$_2$, MoSe$_2$, WS$_2$, WSe$_2$, with a minimal basis (the \textit{d} orbitals for the metal atoms and \textit{p} orbitals for the chalcogen atoms) based on a transformation of the Kohn-Sham density function theory (DFT) hamiltonian to a basis of maximally localized Wannier functions (MLWF). The truncated tight-binding hamiltonian (TBH), with only on-site, first and partial second neighbor interactions, including spin-orbit coupling, provides a simple physical picture and the symmetry of the main band-structure features. Interlayer interactions between adjacent layers are modeled by transferable hopping terms between the chalcogen \textit{p} orbitals. The full-range tight-binding hamiltonian (FTBH) can be reduced to hybrid-orbital k $\cdot$ p effective hamiltonians near the band extrema that captures important low-energy excitations. These \textit{ab-initio} hamiltonians can serve as the starting point for applications to interacting many-body physics including optical transitions and Berry curvature of bands, of which we give some examples.

cond-mat.mes-hall↗