29-30/01/2026 DAEMON General Meeting Belgrade
Join us online at https://undp.zoom.us/j/81268728873?pwd=QJsRzcu9AbDX8qUZUs2rUOOWORJSaj.1&from=addon
Programme:
Day 1:
09.15-9.50 Management committee
9.50-10.00 Break
10.00-11.00 DAEMON COST general discussion
K. Rossi, DAEMON COST: where are we? What do we miss?
11.00-11.30 Coffee Break
11.30-12.00 Research Data Policy Encoding activity
L. Hörmann (online), Project presentation and discussion
12.00-12.30 Machine Learning Interatomic Potentials Benchmark activity
V. Kapil (online), Project presentation and discussion
12.30-14.00 Lunch
14.00-15.00 Materials science and AI in Serbia – part I
Ž. Šljivančanin, Selected Ab Initio Studies of Magnetism and Reactivity at Crystalline Surfaces
S. Stavrić, Emergent and unconventional magnetism in quantum materials
M. Ranđelović, Pd@C hybrid material for enhanced H2O2 reduction: Toward data-driven insights into surface–interface design strategy of electrocatalysts;
A. Sedmak,Rapid Prototyping and Experimental Validation of Digital Twins.
15.00-15.30 Coffee
15.30-16.30 Materials science and AI in Serbia – part II
I.Pašti, Periodic Trends and Defect Selectivity in Two-Dimensional Materials: A Systematic DFT Perspective ;
M.Mitrović-Dankulov, SAIFA – Serbian Artificial Intelligence Factory Antenna;
B.Stanković, A Computational Framework for the ERK2/HDAC4 inhibitors development.
16.30-18.00 poster session and open discussion
Day 2:
09.30-11.00 DAEMON COST general discussion
K. Rossi, DAEMON COST: what’s next?
11.00-11.30 Coffee
11.30-12.30 Multimodal Databases discussion
12.30-14.00 Lunch
14.00-15.00 UNDP Serbia – circular economy and green energy – selected projects
M.Radmilović Artificial Intelligence and Robotics in the Service of Sustainable Agriculture (AgRoAI);
M.Mirković Materials implemented into cosmetic products guided by the principles of the circular economy;
M.Ponjavić, Bacterial Biopolymers in Functional Material Development
M.Mančić, Hydrogen fuel cell integration into the modular solar trigeneration system for heating, cooling and electricity – Energy Cube
15.00-15.30 Coffee
15.30-16.30 Structured networking towards grant applications
Book of Abstract
Selected Ab Initio Studies of Magnetism and Reactivity at Crystalline Surfaces
Željko Šljivančanin
Vinča Institute of Nuclear Sciences, Belgrade, Serbia
Pristine and functionalized crystalline surfaces host atomic sites whose coordination numbers are substantially reduced compared to their bulk counterparts. This reduced coordination leads to distinctive magnetic properties and the emergence of surface atoms with markedly enhanced chemical reactivity. In this talk, I will present examples spanning a range of systems, including structural defects and nanostructures on metal surfaces; adatoms on two-dimensional materials such as graphene and hexagonal boron nitride; ultrathin oxide films; and substitutional impurities in two-dimensional MXenes.
Emergent and unconventional magnetism in quantum materials
Srđan Stavrić
Vinča Institute of Nuclear Sciences, Belgrade, Serbia
Altermagnets are an emerging class of magnetic materials characterized by a collinear compensated arrangement of magnetic moments and a momentum-dependent spin splitting in the band structure. This peculiar combination, arising from specific crystal symmetries, merges the most desirable traits of antiferromagnets – such as ultrafast dynamics without stray fields – with the strong time-reversal-symmetry-breaking responses characteristic for ferromagnets [1]. Found in a diverse range of materials from metals to insulators, altermagnets offer a versatile new platform for next-generation spintronics, paving the way for high-density magnetic memory and terahertz nano-oscillators [2]. This talk will explore the fundamental principles and significant potential of this exciting new class of materials. A special focus will be on recent observation of p-wave magnetism in a spin-spiral type-II multiferroic NiI₂ [3]. We will explore how the symmetry-protected coupling between chirality and polar order enables electrical control of a primarily non-relativistic spin polarization in this material. Finally, we will examine the role of spin-orbit coupling in two-dimensional altermagnets, where this relativistic interaction cannot be neglected as it is the source of the magnetic anisotropy which is crucial for stabilizing long-range magnetic order at finite temperatures [4].
[1] L. Šmejkal, J. Sinova, T. Jungwirth: Phys. Rev. X 12, 031042 (2022)
[2] C. Song, H. Bai, Z. Zhou et al.: Nat. Rev. Mater. 10, 473 (2025)
[3] Q. Song, S. Stavrić, P. Barone, et al.: Nature 642, 64 (2025)
[4] M. Milivojević, M. Orozović, S. Picozzi, M. Gmitra, S. Stavrić: 2D Mater. 11, 035025 (2024)
Pd@C hybrid material for enhanced H2O2 reduction: Toward data-driven insights into surface–interface design strategy of electrocatalysts
Marjan Ranđelović
University of Niš, Faculty of Sciences and Mathematics, Department of Chemistry
Understanding complex surface–interface phenomena is crucial for the rational design of functional electrocatalytic materials. This presentation introduces a Pd@C hybrid material, synthesized via a simple hydrothermal route, and discusses its performance as an efficient electrocatalyst for the hydrogen peroxide reduction reaction (HPRR) in alkaline media. Structural and morphological characterization confirmed the formation of graphene-supported palladium nanoparticles with uniform and catalytically favorable dispersion, providing a well-defined platform for studying adsorption-driven electrochemical mechanisms.
Electrochemical analysis reveals that the enhanced catalytic activity is accompanied with adsorption of both H2O2and O2 on palladium nanoparticles and their intricate interplay through adsorption equilibria, disproportionation reactions, and coupled electron-transfer processes. The resulting mechanism represents a catalytic cycle, where the initial reactant dynamically generates an electroactive intermediate, leading to intertwined reaction pathways manifested as a single voltammetric response.
Beyond the specific Pd@C system, this presentation aims to motivate future efforts toward integrating detailed electrochemical datasets with data science and machine learning methodologies as a promising pathway for advancing the understanding of electrode process kinetics and electrocatalysts design. Such approaches hold the potential to assist in explanation of complex and overlapping reaction pathways, extracting meaningful descriptors of catalytic performance, and guiding the rational design, discovery and optimization of electrocatalysts for sensing and energy-related applications.
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Rapid Prototyping and Experimental Validation of Digital Twins
Aleksandar Sedmak, Miloš Milošević
Innovation Centre of Faculty of Mechanical Engineering
University of Belgrade
This presentation introduces an integrated approach to rapid prototyping and experimental validation of digital twins. By combining additive manufacturing with optical measurement and advanced testing methods, digital models are quickly transformed into physical prototypes and experimentally verified under different type of loadings. The approach enables fast feedback, iterative improvement, and increased confidence in digital twin accuracy. Practical examples demonstrate how this workflow accelerates engineering development and reduces risk.
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Periodic Trends and Defect Selectivity in Two-Dimensional Materials: A Systematic DFT Perspective
Igor A. Pašti
University of Belgrade – Faculty of Physical Chemistry, Belgrade, Serbia
Serbian Academy of Sciences and Arts, Belgrade, Serbia
Density functional theory (DFT) is widely used to study two-dimensional (2D) materials, yet many published results remain difficult to compare due to differences in computational protocols and limited chemical scope. In this lecture, I will present a series of systematic DFT studies on graphene and hexagonal boron nitride that demonstrate how large, internally consistent datasets enable reliable benchmarking and trend extraction. By examining adsorption of broad classes of elements on pristine and defect-engineered 2D surfaces within a unified framework, clear relationships emerge between defect type, adsorption strength, cohesive energy scaling, and electronic-structure modification. The lecture highlights how such systematic approaches provide a robust foundation for predictive modeling, shared databases, and collaborative data-driven research.
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SAIFA – Serbian Artificial Intelligence Factory Antenna
Marija Mitrović-Dankulov, Dušan Vudragović
Scientific Computing Laboratory, Institute of Physics, Belgrade
The Serbian Artificial Intelligence Factory Antenna serves as Serbia’s national hub within the EuroHPC AI Factory ecosystem, supporting the full lifecycle of AI innovation. It provides coordinated access to advanced EuroHPC computing resources, AI tools, curated datasets, and expert consulting for academia, public administration, startups, and industry. By bridging high-performance computing and artificial intelligence, SAIFA enables scalable AI research, domain-specific solutions, and deeper integration of Serbia into the European AI and HPC ecosystem.
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A Computational Framework for the ERK2/HDAC4 inhibitors Development
Branislav Stanković
Department for Nuclear and Plasma Physics, Vinca Institute of Nuclear Sciences –National Institute of the Republic of Serbia, University of Belgrade
In this lecture, we analyze the design of multi-target inhibitors as a basis for future cancertherapies. Specifically, we focus on the development of compounds that simultaneously target ERK2 (extracellular signal-regulated kinase 2) and HDAC4 (histone deacetylase 4). The research integrates machine learning–based QSAR models, molecular docking, and fragment-based drug design using state-of-the-art deep learning approaches, along with quantum chemical calculations and molecular dynamics simulations to validate the findings. Finally, we evaluate ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties as well as synthetic accessibility.
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Artificial Intelligence and Robotics in the Service of Sustainable Agriculture (AgRoAI)
Marija Radmilović, Jelena Ilić
Mihajlo Pupin Institute, Belgrade, Serbia
The agricultural sector is embracing the digital and AI revolution driven by precision agriculture and increased automation. Precision agriculture uses multi-sensor systems and advanced data processing to monitor, detect, treat and intervene in plant health issues. As a country known for its agricultural potential but modest level of technological development, the goal of this project is to: significantly contribute to the adoption of robotics and artificial intelligence in Serbian agriculture, address the labor shortage in seedling care, increase efficiency and productivity in the field, reduce CO2 emissions and provide tools for pesticide-free organic production. The project aims to develop a fully automated seedling production process using robots and automated systems supported by artificial intelligence.
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Materials implemented into cosmetic products guided by the principles of the circular economy
Miljana Mirković
Vinca Institute of Nuclear Sciences –National Institute of the Republic of Serbia, University of Belgrade
Materials obtained through green technology principles enable the innovative repurposing of waste, such as producing completely pure activated carbon from organic coffee grounds via pyrolysis. The green synthesis of hydroxyapatite provides a material with exceptional filtering and antimicrobial properties, while the ethanol remaining from its synthesis is reused for cultivating acetic acid bacteria to produce bacterial cellulose. The materials obtained this way have demonstrated superior performance in skin protection and care. As a result of these projects, three new cosmetic products have been developed following specific technical solutions: Vida sunscreen lotion, Nura skin cleansing milk, and B3iocEll facial masks.
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Bacterial Biopolymers in Functional Material Development
Dr Marijana Ponjavić
Senior Research Associate, Group for Eco-biotechnology and Drug Development, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
Bacterial biopolymers such as bacterial nanocellulose (BNC) and polyhydroxyalkanoates (PHAs) have emerged as versatile, sustainable materials with wide-ranging applications in biomedicine, packaging, and advanced functional systems. Increasing the usage of the bacterial biopolymers as an alternative to commonly used plastic will not only decrease the reliance on fossil-based feedstocks but also reduce the environmental impact and accumulation of resistant plastic waste. Their unique physicochemical properties, high purity, tunable mechanical properties, biocompatibility, and biodegradability, position bacterial biopolymers as the greenest alternatives to conventional synthetic polymers. BNC offers exceptional potential in wound healing, tissue engineering, and flexible electronics, while PHAs provide a biodegradable solution for drug delivery systems and packaging. Recent advancements and efforts have been made in modifying and activating the structure and surface properties of BNC and PHA to boost mechanical properties, improve biodegradability and compatibility with biological systems. Combination of BNC and PHA with biomolecules (such as prodigiosin and actynomycin, pomegranate peel extract) resulted in the new, functional biomaterials of tailored performances beneficial for targeted drug delivery systems, smart packaging and functional food. The integration of machine learning, metabolic modeling, and bioprocess optimization will play a pivotal role in overcoming current limitations for broader application such as production costs, yield variability, and property customization. Supporting the innovations in PHA and BNC based materials are key pillars of the circular bioeconomy in the next-generation functional materials.
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Posters:
Experimental Data across Synthesis, Characterization, and Application
Zorica Mojović, Nataša Jović-Jovičić
University of Belgrade, Institute of Chemistry, Technology and Metallurgy, Department of Catalysis and Chemical Engineering, Njegoševa 12, 11000 Belgrade, Republic of Serbia, zorica.mojovic@ihtm.bg.ac.rs; natasajovicjovicic@ihtm.bg.ac.rs
Our group in the Department of Catalysis and Chemical Engineering at the Institute of Chemistry, Technology and Metallurgy focuses on the synthesis, modification, characterisation, and application of aluminosilicate materials, including clays, zeolites, and alumina. These materials are applied in catalysis, adsorption, and electrochemistry, where structure–property relationships critically determine performance. Experimental studies on these materials generate large and diverse datasets from structural, physicochemical, and functional characterisation. Traditionally, conclusions are drawn using classical data interpretation methods, which are effective when datasets are limited in size and complexity. However, as the number of experimental variables increases, identifying dominant parameters and meaningful correlations becomes increasingly challenging. Principal component analysis (PCA) is commonly used to provide initial insights into parameter influence, yet it often offers only partial or qualitative guidance. Recent advances in artificial intelligence (AI) and machine learning (ML) present new opportunities for deeper and more systematic data analysis. These tools can uncover hidden patterns, nonlinear relationships, and synergistic effects that are inaccessible to conventional methods. Nevertheless, effective implementation of AI and ML requires a shift in research strategy. In particular, data-driven approaches demand larger, more structured, and more diverse datasets than those produced by traditional experimental setups. Therefore, there is a growing need to design new experimental frameworks tailored to AI-assisted analysis. Establishing such workflows will enable more efficient knowledge extraction and accelerate the development of functional aluminosilicate-based materials.
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Macrocyclic Perylene-dimiide Relaxation and Exciton Dynamics
Nikola Fišić and B. Milovanović*
University of Belgrade – Faculty of Physical Chemistry, Studentski trg 12-16, Belgrade, Serbia
* branislavm@ffh.bg.ac.rs
Understanding the impact of molecular structure and thermal fluctuations on exciton formation and relaxation dynamics in macrocyclic perylene diimide (PDI) systems is essential for the rational design of next-generation optoelectronic materials. In this work, we examine two macrocyclic PDI dimers exhibiting distinct degrees of π–π overlap (high and low), which serve as model systems to unravel the fundamental photophysical mechanisms governing exciton coupling, localization, and energy dissipation. Exciton delocalization occurs on markedly different timescales depending on the degree of interchromophoric coupling and structural arrangement. In the strongly coupled system (high degree of overlap), exciton delocalization is achieved only after approximately 170 fs, whereas in the weakly coupled counterpart (low degree of overlap) it occurs within 15 fs. This disparity mirrors the contrasting relaxation dynamics, where stronger coupling leads to slower population decay and more persistent electronic coherence. The results indicate that in systems with enhanced excitonic interactions, exciton localization on individual chromophoric units requires more time than transitions between adiabatic states, reflecting the intricate interplay between electronic and nuclear degrees of freedom. Conversely, in weakly coupled assemblies, rapid localization and energy dissipation are facilitated by reduced electronic coupling and narrower distributions of nonadiabatic coupling terms. Overall, these findings demonstrate how the strength of excitonic coupling and structural flexibility govern the ultrafast evolution of excited states and the transition from delocalized to localized excitonic behavior—an essential insight for understanding and tuning exciton transport in molecular aggregates and optoelectronic materials.
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Altermagnets or weak ferromagnets: the curious cases of 2D fluorides
Marko Orozović1, Timon Moško2, Marko Milivojević3,4, Martin Gmitra2,5, Srdjan Stravrić1
1 Vinča Institute of Nuclear Sciences, University of Belgrade, Serbia, 2 Institute of Physics, Pavol Jožef Šafarik University in Košice, Slovakia, 3 Institute of Informatics, Slovak Academy of Sciences, Slovakia, 4 Faculty of Physics, University of Belgrade, Serbia, 5 Institute of Experimental Physics, Slovak Academy of Sciences, Slovakia
The spin group formalism classifies altermagnets as a new type of collinear magnets with compensated magnetization, based on the assumption of decoupled spin and lattice symmetries. This condition is violated under strong spin-orbit coupling (SOC), an effect especially important in 2D magnets, where magnetic order is stabilized by magnetic anisotropy. Here, we study 2D fluorides (VF4, RuF4, OsF4) and demonstrate their non-relativistic altermagnetic nature, with spin splittings up to 200 meV. However, SOC induces spin canting, transforming these systems into weak ferromagnets. The degree of canting is governed by interplay of Dzyaloshinskii-Moriya interaction and single-ion anisotropy. Our work uncovers a fundamental link between altermagnetism and weak ferromagnetism mediated by SOC, emphasizing that spin-orbit effects must be carefully considered in altermagnetic materials, especially those with heavy elements.
[1] M. Orozović, T. Moško, M. Milivojević, M. Gmitra, S. Stravrić: manuscript in preparation
[2] M. Milivojević, M. Orozović, S. Picozzi, M. Gmitra, S. Stavrić: 2D Mater. 11, 035025 (2024)
[3] J. Sødequist, T. Olsen: Appl. Phys. Lett. 124, 182409 (2024)
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Design of Polyaniline-Based Composites for Electrochemical Applications
B. Kuzmanović1, M. Vujković2, M.M. Ilić1, A. Stevanović3, B.P. Mamula1, M. Vasić2, K. Batalović1
1Department of Nuclear and Plasma Physics, Centre of Excellence for Hydrogen and Renewable Energy, “Vinča” Institute of Nuclear Sciences – National Institute of thе Republic of Serbia, University of Belgrade, POB 522, 11000, Belgrade, Serbia
2Faculty of Physical Chemistry,University of Belgrade, Studentski trg 12-16, 11158 Belgrade 118, Serbia
3The University of Texas at San Antonio, One UTSA Circle, San Antonio TX 78249
Polyaniline (PANI)-based hybrid composites are promising pseudocapacitive materials for electrochemical energy storage due to their high capacity, good conductivity, and tunable interfacial properties. Combining PANI with 2D materials or structured surfaces leads to improved stability and device performance. For PANI–TiO₂ systems, spectroscopic analyses, electrochemical measurements, and density functional theory calculations revealed strong inorganic–organic interfacial interactions that promote electron delocalization and enhanced charge transport. We address the design of MXene–PANI composites using a multimodal machine learning framework based on DFT-derived databases and uMLIPs and word2vec embeddings to identify stable and high-performance combinations that avoid critical row materials. The combined experimental and data-driven approach highlights interfacial engineering and machine learning as effective strategies for optimizing PANI-based composites for supercapacitors and related electrochemical devices.
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Transforming Waste into Advanced Carbon Materials for Water Remediation
Tamara Terzić 1,Vedran Milanković 1, Igor A. Pašti 2,3 and Tamara Lazarević-Pašti 1
1 VINČA Institute of Nuclear Sciences—National Institute of the Republic of Serbia, University of Belgrade, Mike PetrovicaAlasa 12–14, 11000 Belgrade, Serbia
2 Faculty of Physical Chemistry, University of Belgrade, Studentski Trg 12–16, 11158 Belgrade, Serbia;
3 Serbian Academy of Sciences and Arts, Kneza Mihaila 35, 11000 Belgrade, Serbia
The development of sustainable and cost-effective materials for water remediation has motivated increasing interest in the valorization of bio-waste as a resource for functional carbon adsorbents. The research activities presented here focus on the systematic transformation of diverse waste streams, including spent coffee grounds, viscose fibers, immature walnuts, walnut liqueur pomace, and spent mushroom substrate, into engineered carbon materials for the removal of a broad spectrum of water contaminants, such as organophosphorus pesticides, synthetic dyes, antibiotics, and toxic metals. A range of synthetic approaches, including pyrolytic carbonization, chemical activation, hydrothermal carbonization, and hybrid processing routes, has been employed to achieve precise control over pore architecture, surface chemistry, and adsorption selectivity. Structure-function relationships identified across multiple material systems indicate that microporosity predominantly governs the adsorption of small hydrophobic molecules such as chlorpyrifos. At the same time, mesoporosity is critical for the efficient uptake of bulkier contaminants, including dyes. Adsorption mechanisms involve pore filling, π-π interactions between aromatic pollutants and sp² carbon domains, electrostatic effects, and surface complexation mediated by heteroatom-containing functional groups. Across the studied carbon materials, adsorption kinetics are consistently described by pseudo-second-order models, while equilibrium behavior is well captured by Langmuir or Sips isotherms, reflecting the presence of well-defined sorption sites even on heterogeneous surfaces. Several waste-derived carbons demonstrate pronounced selectivity, excellent regenerability over multiple adsorption-desorption cycles, and substantial reductions in pollutant-associated toxicity. In addition, a successful application under dynamic filtration conditions highlights the scalability and practical relevance of these systems. Waste-derived carbon materials represent versatile, tunable, and sustainable platforms for advanced water remediation, effectively aligning materials engineering with circular-economy principles and real-world environmental needs.
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Integrating Data Science in the Design of Biowaste Derived Functional Carbon Materials
Mirjana Medić Ilić, Bojana Paskaš Mamula, Bojana Kuzmanović, Danilo Kisić, Katarina Batalović
Vinča Institute of Nuclear Sciences – National Institute of the Republic of Serbia, University of Belgrade, P.O. Box 522, 11001, Belgrade, Serbia
Carbon materials derived from processed organic waste represent a sustainable and multifunctional platform for environmental remediation and electrochemical energy applications. Their performance is governed by a complex interplay between synthesis conditions, pore architecture, and surface chemistry, making conventional trial-and-error optimization inefficient. Here, we demonstrate how a descriptor-based, data-driven approach enables the systematic identification of key structural and chemical features that control material functionality and process efficiency. In water purification, these descriptors govern adsorption efficiency, kinetics, and selectivity toward aqueous pollutants. Simultaneously, high specific surface area, hierarchical porosity, and tunable surface functionalities enable biowaste-derived carbons to function as electrode materials for supercapacitors and as conductive supports in fuel cell systems, where efficient ion transport, charge storage, and electronic conductivity are critical. By linking synthesis parameters to both adsorption and electrochemical performance through shared descriptors, this approach highlights the potential of bio-derived carbon materials as a unifying platform connecting sustainable waste valorization, environmental technologies, and advanced energy storage and conversion.
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Exploring Metal Hydride Stability through Charge Density Descriptors and Machine Learning Modeling
Bojana Paskaš Mamula, Mirjana Medić Ilić, Bojana Kuzmanović, Katarina Batalović
Center of Excellence for Hydrogen and Renewable Energy CONVINCE, VINČA Institute of Nuclear Sciences – National Institute of the Republic of Serbia
Ongoing efforts toward cleaner energy sources promote hydrogen as a promising energy carrier, making its safe and efficient storage in metal hydrides a key challenge in materials design. Recent advances in machine learning enable reliable prediction of metal hydride properties by leveraging data from diverse materials databases.
In this work, we employ a transfer learning strategy based on atomic embeddings derived from the graph neural network MEGNet to develop a model for predicting hydride formation enthalpy. The model is constructed using the atomic composition and crystal structure of the parent intermetallic compounds, allowing efficient screening of candidate materials. To further enhance physical interpretability and predictive capability, we investigate charge density–based descriptors that capture both electronic and geometric characteristics of metal–hydrogen interactions. By linking these descriptors to machine learning modeling, we explore their influence on the stability of binary and ternary metal hydrides, providing additional insight into the underlying factors governing hydride formation and stability.
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Exploring Single-Element Additions in (Mg1/5Ni1/5Zn1/5Co1/5Cu1/5)O High-Entropy Oxides via Universal MLIPs
Tanja Asanović Antonić, Katarina Batalović
Department of nuclear and plasma physics, „VINČA” Institute of Nuclear Sciences – National Institute of thе Republic of Serbia, University of Belgrade, Belgrade, Serbia
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From Smoke to Soil: AI-Optimized Clay for Environment Clean Air, and Circular Value
Sanja Milošević Govedarović, Tijana Pantić
INS Vinča, University of Belgrade, SERBIA, sanjam@vin.bg.ac.rs
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BATTERY RESEARCH FOR INNOVATIVE GROWTH IN HIGH ENERGY TECHNOLOGIES: LITHIUM-SULFUR AND REDOX FLOW SYSTEMS
Milica M. Vasić
University of Belgrade – Faculty of Physical Chemistry, Belgrade, Serbia
The growing needs for advanced energy storage systems,with better efficiency, sustainability and high-performance, necessitate the development of new materials for battery technologies. The main objective of this project involves the preparation and characterisation of high-efficiency, low-cost and environmentally harmless electrode materials for lithium-sulphur(Li-S) batteries and redoxflow (RFBs) systems.The conditions for preparation of advanced electrode materials will be optimized, including incorporation of various low-cost additives.The project will be realized through multidisciplinary approach, applying various experimental techniques and data processing, through collaboration among three teams with different research skills, fromSlovakia, Serbia and Czech Republic.
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