Electronic structure and topological analysis of charge density of metal-hybride systems with NaCl and rutile crystal structure.

Skladištenje u čvrstom stanju, pre svega u formi metalnih hidrida, predstavlja potencijalno rešenje za bezbedno i efikasno čuvanje vodonika, što je jedan od većih izazova komercijalnoj upotrebi ovog elementa kao goriva i (ili) energetskog vektora. Rezultati istraživanja prikazani u ovom radu pružaju uvid u mehanizme koji određuju različite interakcije u čistim i primesnim metal-hidridnim sistemima – kandidatima za skladištenje vodonika u čvrstom stanju...Metal hydrides represent a promising solution for the safe and efficient solid state storage of hydrogen - one of the major challenges and obstacles for commercial use of this element as a fuel and (or) energy vector. The research results presented in this paper provide a detailed insight into the mechanisms that determine the different interactions in pure and doped metal-hydride systems – potential candidates for hydrogen storage in solid state..

GNN and transfer learning for prediction of formation enthalpy of metal hydrides

Prediction of metal hydride formation enthalpy is one of the key elements for a rapid screening and design of new hydrogen storage materials. In the last decades, DFT (density functional theory) approach showed good predictive potential for the ground state properties and calculation of hydride formation energies. Recently, graph neural network (GNN) implementations show promising results for fast and reliable prediction of formation energies for molecules and crystals. Here, we consider approach for universal machine learning based on a MatErials Graph Network (MEGNet) [1] that enable hydride formation energy prediction with a DFT accuracy. We demonstrate wide screening of potential dopants in Mg2FeH6 and Mg2NiH4. In addition, we study the potential of transfer learning for building the universal machine- learning model capable of addressing experimentally reported hydride formation enthalpies.Solid-State Science & Research ; 10-11th June 2021, Onlin

Property Prediction Using Machine Learning – A Case Study of Metal Hydrides

Accurate prediction of reversible metal hydride formation enthalpy is one of the key requirements for a rapid design of new hydrogen storage and nickel-metal-hydride battery materials. In the last decades, DFT (density functional theory) approach showed good predictive potential for the ground state properties and accurate energies of hydride formation. However, calculating zero-point energy and temperature contributions in addition to 0K formation energy is computationally and time-consuming and therefore often avoided, hindering modelling of experimental behaviour. Recently reported approach for universal machine learning in materials science based on a MatErials Graph Network (MEGNet), an implementation of DeepMind's graph networks, demonstrated very low prediction errors in a broad array of properties in both molecules and crystals, enabling hydride formation energy prediction with a DFT accuracy. In our work, we consider applications of this approach to the wide screening of potential dopants in reversible metal hydride materials, as well as the potential of transfer learning for the universal machine-learning model capable of addressing all contributions to hydrogen formation behaviour. Prediction of the formation energies for the Mg and Ni containing intermetallic hydrides, as well as the influence of various dopants, provides guide to the contribution of chemical nature and local structure to the destabilization of these hydrides

Data science and deep learning for the development of new hydrogen storage materials

Prediction of metal hydride formation enthalpy is one of the key requirements for a rapid design of new hydrogen storage materials. In the last decades, DFT (density functional theory) approach showed good predictive potential for the ground state properties and accurate energies of hydride formation. However, calculating ZPE contribution and temperature effects in addition to formation energy at 0K is computationally and time- consuming and therefore often avoided, resulting in discrepancy to experiment. The development of machine learning and, in particular, deep learning, opens a new perspective for predictive modeling of materials properties. Data collected through DFT calculations can be combined with experimental results in a predictive model, aiming to exploit unexplored compositional space. In this work, we consider the application of MatErials Graph Network (MEGNet) [1] to the prediction of hydrogen formation behavior, and screening of potential dopants in reversible metal hydride materials. Various approaches, relying on transfer learning and both experimental data and computational repositories (MP [2], NOMAD [3]) are proposed as a route to accurate prediction of a structure-property relation for hydrogen storage materials. Domains of applicability of these models are addressed.4th International Meeting MATERIALS SCIENCE FOR ENERGY RELATED APPLICATIONS; September 22-23, 2021; Belgrade, Serbi

High-throughput screening of novel hydrogen storage materials – ML approach

Hydride formation in metals is a widely studied and applied phenomenon necessary to transition to clean energy solutions and various technological applications. We focus on three perspective applications of these materials, namely near-ambient hydrogen storage, hydrogen storage compressor materials, and alkali metal conversion electrodes, to demonstrate acceleration in the research achieved by utilizing a data-driven approach. Graph neural network was developed using a transfer learning approach from the MEGNet model and data related to the thermodynamics of hydride formation obtained in experimental work. Based on the crystal structure and composition as input features, we apply the MetalHydrideEnth model developed in our previous work to predict hydride formation enthalpy in intermetallic compounds. In this work, we focus on demonstrating how this approach, combined with available crystal information obtained from density functional theory calculations, can be applied for fast and extensive searches of novel metal hydride materials, having in mind the above-listed applications.ICCBIKG 2023 : 2nd International Conference on Chemo and Bioinformatics, 28-29 September 2023, Kragujevac, Serbi

Ispitivanje promena na površini nakon dugotrajnog izlaganja vazduhu polazeći od prvih principa-XPS

Within the scope of this paper, a potential impact of noble metal particles on the surface of N-TiO2 and its catalytic properties is observed through correlation with contamination layer thickness. Owing to 'first principle' approach study, without additional experimental measurements or permanent damage to the surface of the samples, it is possible to obtain significant novel information based on a single measurement of the XPS spectra. Presented research demonstrated how the surface contamination layer in the case of samples based on N-TiO2 is related to the nature of two studied noble metals, indicating that Pd might serve as an important co-modifier to suppress surface contamination.Kroz ovaj rad će biti sagledan uticaj čestica plemenitih metala na strukturu površine titanijum dioksida dopiranog azotom i katalitička svojstva kroz uticaj na debljinu sloja nečistoća. U ovakvom pristupu koji polazi od „prvih principa“ je bez dodatnih eksperimentalnih merenja i trajnog oštećenja površine uzoraka moguće dobiti značajne nove informacije korišćenjem rezultata jednom izvedenog merenja rendgenskog fotoelektronskog spektra. Dobijeni rezultati ukazuju na to da promene na površini uzoraka na bazi N-TiO2, do kojih dolazi zbog prisustva Pd, utiču na suzbijanje površinskih organskih nečistoća

Interaction of light alkali metals with ammonia borane: a theoretical study

Ammonia borane – AB (formula: NH3-BH3) has been known for its extraordinary gravimetric hydrogen capacity (nearly 20 wt.%) and is therefore considered as promising hydrogen storage material. However, there are several drawbacks to overcome, namely dehydrogenation kinetics is rather poor, and three-step desorption releases contaminated hydrogen with each subsequent step requiring significantly higher temperature. In addition, there are detrimental by-products (e.g., borasine, diborane) that also limit its practical application. Eliminating at least borasine release is possible through the reaction of alkali metal (M=Li, Na) with AB and producing monometallic amidoborane salts MAB. In this paper, electronic structure calculations and the analysis of charge density topology of pure AB, lithium, and sodium amidoboranes were performed in order to investigate cohesion and bonding nature. The influence of the specific alkali metal substitution will be assessed using calculated IR and Raman spectra and analysis of vibrational bands in comparison to pure AB.4IMMSERA - 4th International Meeting on Materials Science for Energy Related Applications ; September 22-23, 2021 ; Online meeting4th International Meeting on Materials Science for Energy Related Applications held on September 22-23, 2021 at the University of Belgrade, Faculty of Physical Chemistry, Belgrade, Serbia (online meeting) is a satellite event of PHYSICAL CHEMISTRY 2021 15th International Conference on Fundamental and Applied Aspects of Physical Chemistry Organized by UNIVERSITY OF BELGRADE FACULTY OF PHYSICAL CHEMISTRY Belgrade, Serbi

Al-ions Charge Storage Ability of the Conductive Polyaniline Emeraldine Salt

Development of new and attractive generation of polymer devices for application in the field of energy storage that meets the requirements of safety and environmental sustainability is an ongoing challenge. The majority of previous scientific results reported that polyaniline-based supercapacitors use only aqueous acid solutions as electrolyte. [1] The aim of this work is to examine the redox activity of polyaniline emeraldine salt (PANI-ES) in an aqueous electrolyte of aluminum salt, that have been studied to a lesser extent and lacking the characterization of charge storage behavior. The advantage of employing aluminum among various post-lithium rechargeable systems has the advantage in the fact that it is the most abundant metal element in the Earth’s crust with one of the highest gravimetrical and volumetric energy densities. By combining experimental (cyclic voltammetry, chronopotentiometry, galvanic charge/discharge, AFM - Atomic Force Microscopy) and theoretical approaches (density functional theory - DFT), the redox mechanism of polyaniline in the aqueous Al-salt solution is explained. [2] Polyaniline has been shown to have higher Coulombic capacitance at the same charge and discharge current in aqueous aluminum nitrate solution (1M Al(NO3)3) than in hydrogen chloride electrolyte solution (1M HCl), which makes it a suitable electrode for supercapacitors. From a practical point of view, a supercapacitor based on polyaniline and an aqueous solution of Al(NO3)3 was constructed and tested in terms of capacitance, cycle time, and self-discharge. The capacitor shows high charge and discharge capacity (≈269 F g-1 at a current density of 10 A g-1) and relatively good capacity retention after 1000 charge and discharge cycles.COIN2022 - Contemporary Batteries and Supercapacitors - International Symposium ; June 1-2, 2022 ; Belgrade, Serbi

Metal hydrides by design – insights from DFT and data science

Clean energy solutions rely on various hydride materials, for both hydrogen storage and hydrogen production. In our work, we address the possibility of tuning the properties of the most attractive hydrides: Mg-based hydrides, AlH3, and NaBH4, by doping. [...]mESC-IS 2022 : 6th International Symposium on Materials for Energy Storage and Conversion ; July 5-8 2022 ; Brač, Croati

Data-driven Design of New Mg-based Hydride Materials – A Synergy of Experiments and DFT

Hydrogen absorption/desorption is one of the key processes underlying many clean energy applications, such as thermal energy storage, hydrogen storage, hydrogen compression, and nickel-metal hydride batteries. For all those applications fast and reliable characterization of new materials, and in particular, information regarding energetics of hydride formation reaction is of main interest. In the last decades, DFT (density functional theory) approach showed good predictive potential for the ground state properties and calculation of hydride formation energies. Recently, MEGNet implementation of graph neural networks showed promising results for fast and reliable prediction of formation energies for molecules and crystals. Here, we consider the development of a machine learning model based on the available DFT predicted structures and experimentally measured hydride formation enthalpies. The proposed model is capable to predict hydride formation behavior for a wide variety of intermetallic compounds and distinguish the behavior of the polymorphs. In particular, based only on the crystal structure of the starting intermetallic compound, we were able to predict hydride formation enthalpy with accuracy comparable to DFT calculated values. Further, we demonstrate the application of this model for proposing new materials in Mg-Ni-M compound space with the desired enthalpy for hydrogen storage.COIN2022 - Contemporary Batteries and Supercapacitors - International Symposium ; June 1-2, 2022 ; Belgrade, Serbi