arXiv subjects

David Holec

Publications and source records attributed to David Holec.

At least 19 recordsLinked to original sources

arXiv2026

Study of ordering in (MoCrTi)$_{100-x}$Al$_x$ refractory high-entropy alloys using machine learning interatomic potential

Refractory high-entropy alloys are promising candidates for high-temperature applications, yet the effects of composition on their chemical-ordering pathways and mechanical properties remain insufficiently understood. Here, a universal MLIP combined with MC and MD simulations is employed to investigate the temperature-dependent thermodynamic and mechanical behavior of (MoCrTi)(100-x)Alx alloys. Atomic configurations, sublattice occupations, and simulated diffraction intensities reveal pronounced B2-type chemical ordering at low temperatures, with Mo and Al occupying one sublattice and Cr and Ti occupying the other. The configurational heat capacities and SRO parameters further reveal a strong composition dependence of the ordering pathway. The Mo25Cr25Ti25Al25 and Mo32Cr32Ti32Al4 alloys exhibit a single dominant ordering stage involving cooperative changes in multiple B2-type pair correlations. By contrast, Mo28Cr28Ti28Al16 and Mo30Cr30Ti30Al10 exhibit two distinct ordering stages. Their low-temperature features are associated primarily with changes in the Mo-Al and Al-Al correlations, respectively, whereas their high-temperature features involve collective changes in the remaining B2-type pair correlations. Chemical ordering also fundamentally alters the composition dependence of mechanical stiffness. Whereas the elastic constants of disordered configurations increase approximately monotonically with decreasing Al content, those of the ordered configurations exhibit a non-monotonic dependence and reach a maximum in Mo30Cr30Ti30Al10. This anomalous enhancement originates from a SRO induced redistribution of atomic pairs, particularly Mo-Cr pairs. These results establish a direct atomistic connection among alloy composition, multistage chemical ordering, and mechanical stiffness, providing guidance for tuning the mechanical behavior of RHEAs through compositional control.

cond-mat.mtrl-sci↗

arXiv2026

Machine Learning Potential-Driven Molecular Dynamics Simulations of Dehydrogenation in Pristine and Doped MgH$_2$

Machine learning potential-driven molecular dynamics simulations (ML-MD) were employed to provide atomistic insights into the dehydrogenation kinetics of pristine and doped MgH2. Through systematic investigation of distinct surface orientations, the MgH2 (100) surface was identified as the most active low-index surface for hydrogen release. For pristine MgH2, our simulations revealed a novel H2 formation mechanism characterized by H2 generation in the subsurface region followed by diffusion to the surface for desorption, highlighting the critical role of subsurface processes beyond conventional surface-driven pathways. Comprehensive screening of 22 doping elements identified Ni as the most effective dopant. Among several descriptors, machine learning analysis identified the time-coupled Miedema electron density as the critical descriptor, underscoring the role of electronic properties. Consequently, a volcano-shaped relationship was uncovered between the intrinsic Miedema electron density ( nws ) and total hydrogen release (optimal window: 4.0 < nws < 5.4x10-2 e/bohr3). Dopants within this range serve a dual function: acting as thermodynamic sinks for H attraction while maintaining a balanced interaction strength to facilitate H-H coupling and H2 release. This atomistic-level validation provides strong theoretical support for the experimentally observed "hydrogen pump" effect of catalytic phases. The present study demonstrates the strong capability of ML- MD in navigating through complex catalytic mechanisms and establishing quantitative property-activity relationships, providing a robust framework for rational design of high-performance catalysts for MgH2 and other hydrogen storage materials.

cond-mat.mtrl-sci↗

arXiv2026

DFT and MLIP study of solute segregation to coherent and semi-coherent {\alpha}-Fe/Fe$_3$C interfaces

Solute segregation to interfaces significantly impacts material behavior. A large majority of theoretical works focus on grain boundaries and coherent interfaces. Studies on semi-coherent interfaces are usually prohibited by the structural complexity, yielding models beyond the practical capability of density functional theory (DFT), or chemical complexity, restricted by the availability of (classical) interatomic potentials. This work investigates solute segregation to the coherent and semi-coherent $\alpha$-Fe/Fe$_3$C interface in pearlite and its effect on mechanical properties using novel universal machine learning interatomic potentials (uMLIPs). DFT calculated solution enthalpies, segregation energetics, and changes in cohesion at the coherent interface are used to benchmark several state-of-the-art uMLIPs. We find that the GRACE-2L-OAM and GRACE-2L-OMAT models most accurately reproduce the quantum-mechanical predictions. While Cu has the strongest segregation energy of $\approx$ -0.3 eV to the coherent interface among the investigated tramp and trace elements, all of them, As, Cr, Cu, Mo, Ni, P, Sb, and Sn, exhibit significantly more negative segregation values reaching below $\approx$ -1.5 eV in the presence of the misfit dislocation at the semi-coherent interface. The deepest traps are identified in the vicinity of the dislocation core, although the spatial distribution of segregation energies differs markedly among the solute species. The cohesion of the coherent interface is strongly reduced by Sb, Sn, P, and As, and only mildly by Cu, whereas Ni shows a negligible effect, and Cr and Mo slightly enhance cohesion. In contrast, all investigated solutes (except for P) tend to embrittle the semi-coherent interface, with Sn and, especially, Sb having the strongest impact in tensile tests performed in the out-of-plane direction. Abstract shortened for ArXiv.

cond-mat.mtrl-sci↗

arXiv2025

Vacancy-concentration-dependent thermal stability of fcc-(Ti,Al)Nx predicted via chemical-environment-sensitive diffusion activation energies

Thermal decomposition of metastable fcc-(Ti,Al)Nx limits the lifetime of coated components. While energetic decomposition aspects can be modelled reliably, the inherent variability of chemical environment-dependent diffusion activation energies remains systematically unexplored. Here, we predict an activation energy range (envelope) for mass transport in varying chemical environments, reflecting the vacancy concentration range fcc-(Ti0.5Al0.5)1-xNx with x = 0.47, 0.5, 0.53. The stoichiometric compound shows maximum thermal stability, consistent with experimental data. Metal vacancies decrease the average migration energy, while metal and nitrogen vacancies reduce barriers via lattice strain relaxation, enhancing mobility. The strong chemical environment dependence challenges conclusions from single-point activation energy data.

cond-mat.mtrl-sci↗

arXiv2025

Amending CALPHAD databases using a neural network for predicting mixing enthalpy of liquids

In order to establish the thermodynamic stability of a system, knowledge of its Gibbs free energy is essential. Most often, the Gibbs free energy is predicted within the CALPHAD framework using models employing thermodynamic properties, such as the mixing enthalpy, heat capacity, and activity coefficients. Here, we present a deep-learning approach capable of predicting the mixing enthalpy of liquid phases of binary systems that were not present in the training dataset. Therefore, our model allows for a system-informed enhancement of the thermodynamic description to unknown binary systems based on information present in the available thermodynamic assessment. Thereby, significant experimental efforts in assessing new systems can be spared. We use an open database for steels containing 91 binary systems to generate our initial training (and validation) and amend it with several direct experimental reports. The model is thoroughly tested using different strategies, including a test of its predictive capabilities. The model shows excellent predictive capabilities outside of the training dataset as soon as some data containing species of the predicted system is included in the training dataset. The estimated uncertainty of the model is below 1 kJ/mol for the predicted mixing enthalpy. Subsequently, we used our model to predict the enthalpy of mixing of all binary systems not present in the original database and extracted the Redlich-Kister parameters, which can be readily reintegrated into the thermodynamic database file.

physics.chem-ph↗

arXiv2025

Ab initio modeling of TWIP and TRIP effects in $\beta$-Ti alloys

Transformations in bcc-$\beta$, hcp-$\alpha$, and the $\omega$ phases of Ti alloys are studied using Density Functional Theory for pure Ti and Ti alloyed with Al, Si, V, Cr, Fe, Cu, Nb, Mo, and Sn. The $\beta$-stabilization caused by alloying Si, Fe, Cr, and Mo was observed, but the most stable phase appears between the $\beta$ and the $\alpha$ phases, corresponding to the martensitic $\alpha''$ phase. Next, the $\{112\}\langle11\bar1\rangle$ bcc twins are separated by a positive barrier, which further increases by alloying w.r.t. pure Ti. The $\{332\}\langle11\bar3\rangle$ twinning yields negative barriers for all species but Mo and Fe. This is because the transition state is structurally similar to the $\alpha$ phase, which is preferred over the $\beta$ phase for the majority of alloying elements. Lastly, the impact of alloying on twin boundary energies is discussed. These results may serve as design guidelines for novel Ti-based alloys with specific application areas.

cond-mat.mtrl-sci↗

arXiv2025

Interstitials as a key ingredient for P segregation to grain boundaries in polycrystalline $\alpha$-Fe

Solute segregation to grain boundaries (GBs) significantly impacts material behavior, with most studies focusing on substitutional solute segregation while neglecting interstitial segregation due to its increased complexity. The site preference, interstitial or substitutional, for P segregation in $\alpha$-Fe still remains under debate. This work investigates both substitutional and interstitial GB segregation in a polycrystalline model using classical interatomic potentials and machine learning. The method is validated with H and Ni, whose segregation behaviors are well understood. For P, we find segregation to both GB site types, with a preference for substitutional sites based on mean segregation energy. However, the abundance of interstitial sites means interstitial segregation also significantly contributes to the GB enrichment with P. This highlights the importance of considering interstitial P segregation alongside substitutional segregation. Additionally, obtaining a representative spectrum of segregation energies is crucial for accurate, experimentally aligned predictions.

cond-mat.mtrl-sci↗

arXiv2024

Revealing trends in catalytic activity of adatoms for hydrogen adsorption on carbon: a case study of graphene and carbon nanotube

The increasing demand for sustainable energy solutions necessitates advancements in hydrogen storage technologies. This study investigates the hydrogen adsorption characteristics of graphene and a (8,0) carbon nanotube (CNT) decorated with adatoms of various elements. Using molecular dynamics (MD) simulations and the universal interatomic potential 'PreFerred Potential' (PFP) implemented in the Matlantis framework, we explore the hydrogen storage capabilities of these doped carbon structures at 77K. We analyze the adsorption efficiency based on the position of adatoms (top, bridge, and hollow sites) and find that the group II elements, such as calcium and strontium, exhibit significant hydrogen uptake. Additionally, light elements like lithium and sodium demonstrate enhanced gravimetric hydrogen storage due to their low atomic mass. Our findings provide insights into the potential of doped graphene and CNTs for efficient hydrogen storage applications.

cond-mat.mtrl-sci↗

arXiv2024

Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: a case study of silicon nitride

Amorphous silicon nitride (a-SiN) is a material which has found wide application due to its excellent mechanical and electrical properties. Despite the significant effort devoted in understanding how the microscopic structure influences the material performance, many aspects still remain elusive. If on the one hand \textit{ab initio} calculations respresent the technique of election to study such a system, they present severe limitations in terms of the size of the system that can be simulated. Such an aspect plays a determinant role, particularly when amorphous structure are to be investigated, as often results depend dramatically on the size of the system. Here, we overcome this limitation by training a machine-learning (ML) interatomic model to \textit{ab initio} data. We show that molecular dynamics simulations using the ML model on much larger systems can reproduce experimental measurements of elastic properties, including elastic isotropy. Our study demonstrates the broader impact of machine-learning potentials for predicting structural and mechanical properties, even for complex amorphous structures.

cond-mat.mtrl-sci↗

arXiv2024

Segregation to grain boundaries in disordered systems: an application to a Ni-based multi-component alloy

Segregation to defects, in particular to grain boundaries (GBs), is an unavoidable phenomenon leading to changed material behavior over time. With the increase of available computational power, unbiased quantum-mechanical predictions of segregation energies, which feed classical thermodynamics models of segregation (e.g., McLean isotherm), become available. In recent years, huge progress towards predictions closely resembling experimental observations was made by considering the statistical nature of the segregation process due to competing segregation sites at a single GB and/or many different types of co-existing GBs. In the present work, we further expand this field by explicitly showing how compositional disorder, present in real engineering alloys (e.g. steels or Ni-based superalloys), gives rise to a spectrum of segregation energies. With the example of a $\Sigma 5$ GB in a Ni-based model alloy (Ni-Co-Cr-Ti-Al), we show that the segregation energies of Fe, Mn, W, Nb, and Zr are significantly different from those predicted for pure elemental Ni. We further use the predicted segregation energy spectra in a statistical evaluation of GB enrichment, which allows for extracting segregation enthalpy and segregation entropy terms related to the chemical complexity in multi-component alloys.

cond-mat.mtrl-sci↗

arXiv2024

Prediction and identification of point defect fingerprints in X-ray photoelectron spectra of TiN$_x$ with 1.18 $\le x \le$ 1.37

We investigate the effect of selected N and Ti point defects in $B$1 TiN on N 1s and Ti\,2p$_{3/2}$ binding energies (BE) by experiments and ab initio calculations. X-ray photoelectron spectroscopy (XPS) measurements of TiN$_x$ films with 1.18 $\le x \le$ 1.37 reveal additional N 1s spectral components at lower binding energies. Ab initio calculations predict that these components are caused by either Ti vacancies, which induce a N 1s BE shift of -0.54 eV in its first N neighbors, and/or N tetrahedral interstitials, which have their N 1s BE shifted by -1.18 eV and shift the BE of their first N neighbors by -0.53 eV. However, based on {\it ab initio} data the tetrahedral N interstitial is estimated to be unstable at room temperature. We, therefore, unambiguously attribute the N 1s spectral components at lower BE in Ti-deficient TiN$_x$ thin films to the presence of Ti vacancies. Furthermore, it is demonstrated that the vacancy concentration in Al-capped Ti-deficient TiN$_x$ can be quantified with the here proposed correlative method based on measured and predicted BE data. Our work highlights the potential of ab initio-guided XPS measurements for detecting and quantifying point defects in $B$1 TiN$_x$.

cond-mat.mtrl-sci↗

arXiv2024

Interplay between alloying and tramp element effects on temper embrittlement in bcc iron: DFT and thermodynamic insights

The details of the temper embrittlement mechanism in steels caused by impurities are unknown. Especially from an atomistic point of view, there are still open questions regarding their interactions with alloying elements such as Ni, Cr, and Mo. Therefore, we used density functional theory to investigate the segregation and co-segregation behavior and the resulting influence on the cohesion of three representative tilt grain boundaries in iron. The results are implemented in a multi-site and multi-component kinetic and thermodynamic model for grain boundary segregation, to gain insights into the temporal and final grain boundary coverage. Our results show that the segregation tendency of As, Sb, and Sn is stronger than that of the alloying elements and significantly mitigates the grain boundary cohesion. Depending on the GB type, interactions between Sb and Sn vary from negligible to strongly attractive, which increases the likelihood of co-segregation. The cohesion-weakening effect is further amplified when elements such as Sb, Sn, and As co-segregate, compared to their individual segregation. In contrast, the co-segregation of Ni and Cr does not significantly increase the enrichment of impurities at grain boundaries, and their impact on cohesion is found to be negligible. The ability of Mo to mitigate reversible temper embrittlement is primarily attributed to its cohesion-enhancing effect and its capability to repel tramp elements from GBs, rather than scavenging them within the bulk, as suggested by previous literature.

cond-mat.mtrl-sci↗

arXiv2024

A Predictive Model for Catalytic Methane Pyrolysis

Methane pyrolysis provides a scalable alternative to conventional hydrogen production methods, avoiding greenhouse gas emissions. However, high operating temperatures limit economic feasibility on an industrial scale. A major scientific goal is, therefore, to find a catalyst material that lowers operating temperatures, making methane pyrolysis economically viable. In this work, we derive a model that provides a qualitative comparison of possible catalyst materials. The model is based on calculations of adsorption energies using density functional theory. Thirty different elements were considered. Adsorption energies of intermediate molecules in the methane pyrolysis reaction correlate linearly with the adsorption energy of carbon. Moreover, the adsorption energy increases in magnitude with decreasing group number in the $d$-block of the periodic table. For a temperature range between 600 and 1200 K and a normalized partial pressure range for $\mathrm{H_2}$ between $10^{-1}$ and $10^{-5}$, a total of eighteen different materials were found to be optimal catalysts at least once. This indicates that catalyst selection and reactor operating conditions should be well-matched. The present work establishes the foundation for future large-scale studies of surfaces, alloy compositions, and material classes using machine learning algorithms.

cond-mat.mtrl-sci↗

arXiv2023

On energetics of allotrope transformations in transition-metal diborides via plane-by-plane shearing

Transition metal diborides crystallise in the $\alpha$, $\gamma$, or $\omega$ type structure, in which pure transition metal layers alternate with pure boron layers stacked along the hexagonal [0001] axis. Here we view the prototypes as different stackings of the transition metal planes and suppose they can transform from one into another by a displacive transformation. Employing first-principles calculations, we simulate sliding of individual planes in the group IV-VII transition metal diborides along a transformation pathway connecting the $\alpha$, $\gamma$, and $\omega$ structure. Chemistry-related trends are predicted in terms of energetic and structural changes along a transformation pathway, together with the mechanical and dynamical stability of the different stackings. Our results suggest that MnB$_2$ and MoB$_2$ possess the overall lowest sliding barriers among the investigated TMB$_2$s. Furthermore, we discuss trends in strength and ductility indicators, including Young's modulus or Cauchy pressure, derived from elastic constants.

cond-mat.mtrl-sci↗

arXiv2021

$\textit{Ab initio}$-guided X-ray photoelectron spectroscopy quantification of Ti vacancies in Ti$_{1-\delta}$O$_x$N$_{1-x}$ thin films

$\textit{Ab initio}$ calculations were employed to investigate the effect of oxygen concentration dependent Ti vacancies formation on the core electron binding energy shifts in cubic titanium oxynitride (Ti$_{1-\delta}$O$_x$N$_{1-x}$). It was shown, that the presence of a Ti vacancy reduces the 1s core electron binding energy of the first N neighbors by $\sim$0.6 eV and that this effect is additive with respect to the number of vacancies. Hence it is predicted that the Ti vacancy concentration can be revealed from the intensity of the shifted components in the N 1s core spectra region. This notion was critically appraised by fitting the N 1s region obtained via X-ray photoelectron spectroscopy (XPS) measurements of Ti$_{1-\delta}$O$_x$N$_{1-x}$ thin films deposited by high power pulsed magnetron sputtering. A model to quantify the Ti vacancy concentration based on the intensity ratio between the N 1s signal components, corresponding to N atoms with locally different Ti vacancy concentration, was developed. Herein a random vacancy distribution was assumed and the influence of surface oxidation from atmospheric exposure after deposition was considered. The so estimated vacancy concentrations are consistent with a model calculating the vacancy concentration based on the O concentrations determined by elastic recoil detection analysis and text book oxidation states and hence electroneutrality. Thus, we have unequivocally established that the formation and population of Ti vacancies in cubic Ti$_{1-\delta}$O$_x$N$_{1-x}$ thin films can be quantified by XPS measurements from N 1s core electron binding energy shifts.

cond-mat.mtrl-sci↗

arXiv2020

Enhanced fracture toughness in ceramic superlattice thin films: on the role of coherency stresses and misfit dislocations

Superlattice (SL) thin films composed of refractory ceramics unite extremely high hardness and enhanced fracture toughness; a material combination often being mutually exclusive. While the hardness enhancement obtained whentwo materials form a superlattice is well described by existing models based on dislocation mobility, the underlying mechanisms behind the increase in fracture toughness are yet to be unraveled. Here we provide a model based on linear elasticity theory to predict the fracture toughness enhancement in (semi-)epitaxial nanolayers due to coherency stresses and formation of misfit dislocations. We exemplarily study a superlattice structure composed of two cubic transition metal nitrides (TiN, CrN) on a MgO (100) single-crystal substrate. Minimization of the overall strain energy, each time a new layer is added on the nanolayered stack, allows estimating the density of misfit dislocations formed at the interfaces. The evolving coherency stresses, which are partly relaxed by the misfit dislocations, are then used to calculate the apparent fracture toughness of respective SL architectures by applying the weight function method. The results show that the critical stress intensity increases steeply with increasing bilayer period for very thin (essentially dislocation-free) SLs, before the K_IC values decline more gently along with the formation of misfit dislocations. The characteristic K_IC vs. bilayer-period-dependence nicely matches experimental trends. Importantly, all critical stress intensity values of the superlattice films clearly exceed the intrinsic fracture toughness of the constituting layer materials, evincing the importance of coherency stresses for increasing the crack growth resistance.

cond-mat.mtrl-sci↗

arXiv2019

Point-defect engineering of MoN/TaN superlattice films: A first-principles and experimental study

Superlattice architecture represents an effective strategy to improve performance of hard protective coatings. Our model system, MoN/TaN, combines materials well-known for their high ductility as well as a strong driving force for vacancies. In this work, we reveal and interpret peculiar structure-stability-elasticity relations for MoN/TaN combining modelling and experimental approaches. Chemistry of the most stable structural variants depending on various deposition conditions is predicted by Density Functional Theory calculations using the concept of chemical potential. Importantly, no stability region exists for the defect-free superlattice. The X-ray Diffraction and Energy-dispersive $\text{X-ray}$ Spectroscopy experiments show that MoN/TaN superlattices consist of distorted fcc building blocks and contain non-metallic vacancies in MoN layers, which perfectly agrees with our theoretical model for these particular deposition conditions. The vibrational spectra analysis together with the close overlap between the experimental indentation modulus and the calculated Young's modulus points towards MoN$_{0.5}$/TaN as the most likely chemistry of our coatings.

cond-mat.mtrl-sci↗

arXiv2019

Disruption of the $sp^2$ bonding by the compression of the $\pi$-electronic orbitals of graphene at various stacking orders

We investigate the behaviour of the $\pi$-electrons under compression and the effect of the stacking order of graphene layers. First we find that electrons can hardly be squeezed through the $sp^2$ network, regardless of the stacking order. The largely deformed electronic orbitals (mainly those of $\pi$-electrons) under compression along the $\textit{c}$-axis increase interlayer interaction between graphene layers as expected, but surprisingly in a similar way for the A-A and Bernal stacking. On the other hand, the large out-of-plane compression shifts the in-plane phonon frequencies of A-A stacked graphene layers significantly and very differently from Bernal stacked layers. We attribute these results to the $sp^2$-electrons filling the low-density central area in a carbon hexagon under compression for the A-A stacking, hence resulting in a non-monotonic change of the $sp^2$-bonding. The results strongly suggest not to ignore 3D features of a 2D material.

cond-mat.mtrl-sci↗