Modelling and Simulation in Materials Science and Engineering

In this study, we examine grain boundary energy as a function of both misorientation and inclination using a phase-field approach. Through systematic investigation of their independent and combined influence, we observe that misorientation appears to influence energetic preferences, with an increased frequency (approximately 35%) of low-angle boundaries (0–5^∘), while inclination tends to affect local boundary geometry, with boundary planes showing preference for 45–50^∘ inclination angles. Our simulations suggest that while inclination dependence influences boundary morphology, the combined effect leads to morphological features including elongated grains and distinctive growth kinetics. Notably, inclination-dominated cases show slower growth rates compared to isotropic or misorientation-dependent systems, though misorientation-dependent cases exhibit faster decrease in interface energy density. The model's behavior is examined through studies of facet formation in cubic systems and configurations at multiple grain junctions. Statistical analysis of two-dimensional polycrystalline systems indicates that the interaction between misorientation and inclination may be more intricate than previously considered, suggesting potential value in incorporating both factors when studying anisotropic grain growth. These observations contribute to our developing understanding of microstructural evolution in polycrystalline materials.

This study investigates the creep and damage behaviour of a thick-walled spherical vessel subjected to severe thermal conditions in a hypothetical scenario. The vessel experiences an abrupt internal temperature change, potentially leading to catastrophic failure. In this scenario, the continuous growth of plastic deformation due to the drastic temperature increase is identified as the primary cause of vessel failure. To address this, the finite element method was employed to simulate the transient thermal response and plastic creep strain within the vessel. Uni-axial creep experiments were carried out to determine the material's creep characteristics. A damage criterion was utilized to predict the onset of catastrophic failure. An approximate experimental method was employed to confirm the predicted damage initiation in the numerical solutions. The methodology applied in this research enables accurate forecasting of the moment when damage initiation occurs. The finding emphasizes that vessels used in nuclear reactors must be designed with consideration of the probability of unexpected and severe temperature changes.

Water originating from underground frequently leads to severe corrosion of subsurface equipment and tubing, increasing the risks of structural failure, pipeline leakage, and environmental contamination, which can endanger both operational safety and environmental integrity. To delve into the galvanic corrosion of downhole equipment and tubing, a comprehensive study was conducted using COMSOL simulations to explore the galvanic corrosion mechanism between 316L/40Cr and Ni–P coated 42CrMo alloys. According to the electrochemical tests, a two-dimensional galvanic corrosion model encompassing 316L/40Cr, Ni–P coating, and 42CrMo substrate was established. The mechanisms of galvanic corrosion under varying material and coating damage scenarios were extensively discussed and analyzed. The findings reveal that, when the Ni–P coating remains intact, 40Cr undergoes more pronounced corrosion (approximately 35.3 µm) compared to 316L (about 2.11 µm). However, upon coating damage, 316L becomes the cathode, while 40Cr and 42CrMo function as anodes. Notably, the total current in the coupled system (0.475 mA) comprising 316L, 42CrMo, and Ni–P coating is lower than that in the system (0.712 mA) with 40Cr, 42CrMo, and Ni–P coating. Additionally, an increase in the surface area of the 316L electrode correlates with an enhanced corrosion rate, manifesting in corrosion depths of 32.09–32.16 µm, 31.3–31.2 µm, and 30.172–30.312 µm at 400, 300, and 200 µm, respectively.

High accuracy force-field parameters of (RDX, TATB)/Estane interfaces are fitted from the ab initio energy curves of various adsorption configurations, with rigorous validation using an independent test set. Employing this force-field in molecular dynamics, we calculate a set of thermodynamic properties for plastic bonded explosives (PBXs), including the heat capacity, thermal expansion coefficient, bulk modulus, Grüneisen coefficient and Hugoniot parameters. Three critical dynamic behaviors are obtained: internal pressure, temperature-dependent debonding and elastic–plastic transition. First, the thermal expansion difference across interface generates internal pressure (0.6–0.8 GPa) for particles, which counteracts volume expansion. Second, the interfacial debonding occurs at 450 K for Estane-coated TATB and 400 K for Estane-coated RDX. Third, the tensile strain thresholds of void formation are quantified as 0.042 and 0.073 for Estane-coated TATB/RDX respectively, relevant to the elastic–plastic transition of material. The refined interfacial force-field demonstrates broad applicability in material design and property prediction, particularly for elucidating PBX failure mechanisms such as stress accumulation, defect evolution and debonding.

In this theoretical study, we employed first-principles density functional theory calculations to investigate the structural, electronic, optical, and photocatalytic properties of pristine and nitrogen-doped (0,5) TiO₂ anatase nanotubes. Bandgap narrowing in nitrogen-doped TiO₂ nanotubes is attributed to the introduction of electronic states above the valence band, arising from the interaction between nitrogen dopants and the TiO₂ lattice. These states shift the valence band maximum to the higher values relative to the pristine nanotube, yielding optimal narrowed bandgaps of 2.8 eV, 1.0 eV, and 2.76 eV for substitutional, interstitial, and mixed doping models, respectively. Substitutional models with 0.83 at. % nitrogen demonstrates favorable band positions for comprehensive water splitting and CO₂ reduction. Based on the previous studies on the electronic structures of FePS₃ (Co₉Se₈), we demonstrate the enhanced photocatalytic performance of the interstitial model in forming an S-scheme heterojunction, which promotes efficient hydrogen production and CO₂ reduction. These findings elucidate the critical role of nitrogen doping in augmenting the photocatalytic efficiency of TiO₂ nanotubes, providing a theoretical foundation for future experimental validation.

An interatomic potential, traditionally regarded as a mathematical function, serves to depict atomic interactions within molecules or solids by expressing potential energy concerning atom positions. These potentials are pivotal in materials science and engineering, facilitating atomic-scale simulations, predictive material behavior, accelerated discovery, and property optimization. Notably, the landscape is evolving with machine learning transcending conventional mathematical models. Various machine learning-based interatomic potentials, such as artificial neural networks, kernel-based methods, deep learning, and physics-informed models, have emerged, each wielding unique strengths and limitations. These methods decode the intricate connection between atomic configurations and potential energies, offering advantages like precision, adaptability, insights, and seamless integration. The transformative potential of machine learning-based interatomic potentials looms large in materials science and engineering. They promise tailor-made materials discovery and optimized properties for specific applications. Yet, formidable challenges persist, encompassing data quality, computational demands, transferability, interpretability, and robustness. Tackling these hurdles is imperative for nurturing accurate, efficient, and dependable machine learning-based interatomic potentials primed for widespread adoption in materials science and engineering. This roadmap offers an appraisal of the current machine learning-based interatomic potential landscape, delineates the associated challenges, and envisages how progress in this domain can empower atomic-scale modeling of the composition-processing-microstructure-property relationship, underscoring its significance in materials science and engineering.

Science is and always has been based on data, but the terms 'data-centric' and the '4th paradigm' of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

Materials and manufacturing engineering are continuing to advance in part to computational materials and process modeling and associated linkages with associated interdisciplinary efforts across all engineering, manufacturing, and quality disciplines. Computational modeling has enabled virtual processing, prediction and assessment of potential new materials and manufacturing processes, without or with limited need to perform costly and time-consuming physical trials. Development and integration of computational materials and process engineering requires a number of seemingly disparate critical technical elements, making this evolving computational capability very complicated. Accurate and validated models are supporting rapid material, process, and component development, and additionally qualification and certification of new final products through integrated computational materials engineering (ICME). These capabilities are driving further industrial utilization of computational material and process modeling with formalized linkages and integration within multidisciplinary engineering workflows. Past utilization, present applications and potential future development activities indicate that industry has now fully embraced the tools and methods, and overarching engineering framework of ICME.

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry, and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing.

The irradiation of the target surface by an ultrafast femtosecond (fs) laser pulse produces the extreme non-equilibrium states of matter and subsequent phase transformations. Computational modeling and simulation is a very important tool for gaining insight into the physics processes that govern the laser–matter interactions, and, specifically, for quantitative understanding the laser light absorption, electron–ion energy exchange, spallation, melting, warm dense matter regime, vaporization, and expansion of plasma plume. High-fidelity predictive modeling of a variety of these multi-physics processes that take place at various time and length scales is extremely difficult, requiring the coupled multi-physics and multi-scale models. This topical review covers progress and advances in developing the modeling approaches and performing the state-of-the-art simulations of fs laser-pulse interactions with solids and plasmas. A complete kinetic description of a plasma based on the most accurate Vlasov–Maxwell set of equations is first presented and discussed in detail. After that an exact kinetic model that encompasses the microscopic motions of all the individual particles, their charge and current densities, generated electric and magnetic fields, and the effects of these fields on the motion of charged particles in a plasma is briefly reviewed. The methodology of kinetic particle-in-cell (PIC) approach that is well suitable for computational studies of the non-linear processes in laser–plasma interactions is then presented. The hydrodynamic models used for the description of plasmas under the assumption of a local thermodynamic equilibrium include the two-fluid and two-temperature model and its simplifications. The two-temperature model coupled with molecular dynamics (MD) method is finally discussed. Examples are illustrated from research areas such as applications of the fully kinetic, PIC, hydrodynamic, and MD models to studies of ultrafast laser–matter interactions. Challenges and prospects in the development of computational models and their applications to the modeling of ultrafast intense laser–solid and laser–plasma interactions are overviewed.

The utilization of diamond as substrate for GaN power devices is regarded as a promising heat dissipation measure. However, the bonding interface between diamond and GaN wafers is prone to defects and significant interfacial thermal resistance (ITR). This issue severely compromises the thermal performance of the devices. This paper focuses on the urgent problems that need to be solved in high electron mobility transistors manufacturing, and systematically studies the influence of micro random vacancy defects on the heat transfer performance of GaN/diamond interface using non-equilibrium molecular dynamics method. The intrinsic mechanism was analyzed through radial distribution function and phonon density of states at the micro scale. The results showed that the ITR decreases gradually with the increase of random vacancy defect density in GaN. When the defect concentration reached 12%, the ITR was 11.89 (m²K)/GW, a decrease of 69%. Similarly, the increase of random defects in diamond will also reduce ITR. When the concentration of vacancy defects in diamond increased from 0 to 12%, the ITR decreased from 20.12 (m²K)/GW to 13.05 (m²K)/GW, a decrease of 54%. The research provides a theoretical basis for the thermal design of GaN/diamond devices.

This work focuses on optimizing various growth parameters related to a novel pendeo-epitaxy manufacturing approach aimed at producing high quality gallium nitride (GaN) for optoelectronic applications. The proposed approach consists of growing GaN pyramids on top of GaN/AlN/Si(111)/SiO2 etched nanopillars on Silicon-on-Insulator substrates. It is expected that the excess of energy on the vertical edge of the GaN pyramids will allow the pillars to rotate so that the GaN on the top can align crystallographically. This should result in the formation of well-oriented GaN layer with low dislocation density. Finite element simulations using Abaqus software are performed to determine the optimal pillar parameters (e.g. radius (r), length, center-to-center distance between two pillars (pitch)) that would reduce the required rotation energy for it to be lower than the available energy, thus making the rotation of the pillars energetically feasible. The results showed that the pitch and the length of the pillars have the least effect on the required rotation energy while the latter is proportional to the square of the rotation angle. These numerical results allowed the development and validation of a simplified analytical model that only accounts for the mechanics of the nanopillar in both tilt and twist case. The analytical formulation demonstrated that the most critical parameter is the radius as the required rotation energy is proportional to r4. Finally, this work allowed us to predict the optimal strategy for designing samples to enable the growth of high quality GaN layers suitable for microLEDs.

Composition inhomogeneities arise in multicomponent alloys during processing, e.g., spinodal alloys, during rapid solidification, e.g., in additive manufactured alloys, and/or when the alloys are subjected to extreme conditions, such as irradiation. These inhomogeneities have a strong impact on the mechanical response and strength of the alloys. In this paper, a framework for studying single crystal plasticity in inhomogeneous alloys using discrete dislocation dynamics (DDD) is presented. Virtual realizations of a single Fourier mode sinusoidal and stochastic composition fields are generated for testing the impact of composition inhomogeneity within a three-dimensional DDD framework. The composition fields are also utilized to determine internal coherency stress arising due to lattice parameter dependence on composition by solving an eigenstrain boundary value problem (BVP). Composition-aware dislocation velocities, determined from molecular dynamics (MD) simulations are utilized to model the composition dependent local lattice mobility of dislocations. The composition reconstruction scheme, internal coherency stress, and the composition-dependent dislocation velocities are coupled to DDD, and the effects of the composition fluctuations are studied on the stress-strain response and evolution of the dislocation densities in a model BCC FeCrAl alloy. The effects of the composition fluctuations on crystal plasticity are studied from the perspective of single dislocation and their collective dynamics. The influence of the inhomogeneous composition fields on the collective dynamics of dislocations is revealed through the statistics of cross-slip and the driving forces on the dislocations coming from the stress associated with composition fields, applied load, and dislocation-dislocation interactions.

An interatomic potential for the Ti-V binary alloy focusing on evolution of defects, including ones arising as a result of the irradiation process, was constructed within the Lipnitskii-Saveliev approach, which accurately takes into account three-particle interactions and the sum of all multi-particle interactions of a higher order in the framework of the centrally symmetric approximation. In the new potential, Ti-V interactions were fitted to the DFT data on set of model structures with different coordination numbers, including ones with vacancies. The properties used for fitting are accurately reproduced by the present potentials for both pure elements and alloy systems. The potential was tested on the binding energies between Ti atoms and self-point defects in bcc V, elastic moduli, thermal expansion and melting point of some alloys, and diffusion. We obtained qualitative agreement for these properties with available theoretical and experimental data. Finally, we investigated evolution of excess vacancies in the V-4 at. \% Ti alloy at 700 K, which are typical conditions of vanadium-based alloys for fusion applications. We found that no vacancy loop is formed in the alloy in contrast to the pure V, which agrees with the experimental observations. The potential is expected to be especially suitable for irradiation simulations of vanadium based V-Ti alloys.

In this study, we examine grain boundary energy as a function of both misorientation and inclination using a phase-field approach. Through systematic investigation of their independent and combined influence, we observe that misorientation appears to influence energetic preferences, with an increased frequency (approximately 35%) of low-angle boundaries (0–5^∘), while inclination tends to affect local boundary geometry, with boundary planes showing preference for 45–50^∘ inclination angles. Our simulations suggest that while inclination dependence influences boundary morphology, the combined effect leads to morphological features including elongated grains and distinctive growth kinetics. Notably, inclination-dominated cases show slower growth rates compared to isotropic or misorientation-dependent systems, though misorientation-dependent cases exhibit faster decrease in interface energy density. The model's behavior is examined through studies of facet formation in cubic systems and configurations at multiple grain junctions. Statistical analysis of two-dimensional polycrystalline systems indicates that the interaction between misorientation and inclination may be more intricate than previously considered, suggesting potential value in incorporating both factors when studying anisotropic grain growth. These observations contribute to our developing understanding of microstructural evolution in polycrystalline materials.

This study investigates the creep and damage behaviour of a thick-walled spherical vessel subjected to severe thermal conditions in a hypothetical scenario. The vessel experiences an abrupt internal temperature change, potentially leading to catastrophic failure. In this scenario, the continuous growth of plastic deformation due to the drastic temperature increase is identified as the primary cause of vessel failure. To address this, the finite element method was employed to simulate the transient thermal response and plastic creep strain within the vessel. Uni-axial creep experiments were carried out to determine the material's creep characteristics. A damage criterion was utilized to predict the onset of catastrophic failure. An approximate experimental method was employed to confirm the predicted damage initiation in the numerical solutions. The methodology applied in this research enables accurate forecasting of the moment when damage initiation occurs. The finding emphasizes that vessels used in nuclear reactors must be designed with consideration of the probability of unexpected and severe temperature changes.

The dynamics of dislocations can be formulated in terms of the evolution of continuous variables representing dislocation densities ('continuum dislocation dynamics'). We show for various variants of this approach that the resulting models can be envisaged in terms of the evolution of order-parameter-like variables that strive to minimize a free energy functional which incorporates interface energy-like terms, i.e. as a phase field theory. We show that dislocation density variables obey non-standard conservation laws. These lead, in conjunction with the externally supplied work, to evolution equations that go beyond the classical framework of Allen-Cahn vs. Cahn–Hilliard equations. The approach is applied to the evolution of dislocation patterns in materials with B1(NaCl) lattice structure and it is demonstrated that it gives access to the formation of cellular dislocation patterns, and the concomitant emergence of both incidental and geometrically necessary dislocation boundaries.

Bearing steels are complex materials composed of an iron matrix and a well defined and precise amount of several alloying elements. In order to improve sustainability and circularity, there is a tendency to increase the utilisation of scrap material for their production. The variability of the composition of scrap material has a direct impact on the properties of the final steels: There is less control on their composition due to the possible presence of larger amounts of tramp and alloying elements. One way to study the effect of tramp elements is by using universal machine learning interatomic potentials. These types of potential render the investigation of multi-element systems possible. They permit the study of interactions between iron atoms in the matrix and multiple concurrent tramp and alloying elements, a feature that is currently not available in classical potentials. In this work, we present a benchmark of four state-of-the-art universal machine learning interatomic potentials (Crystal Hamiltonian Graph Neural Network (Deng et al 2023 Nat. Mach. Intell.5 1031–41) (v0.2.0 and v0.3.0), Materials 3-body Graph Network (Chen and Ping Ong 2022 Nat. Comput. Sci.2 718–28), Multiple Atomic Cluster Expansion (Batatia et al 2022 Advances in Neural Information Processing Systems vol 35 pp 11423–36)) and SevenNet (Park et al 2024 J. Chem. Theory Comput.20 4857–68), and study their applicability to the simulation of systems relevant to steels. For pure Fe, all potentials accurately predict the equilibrium lattice parameter, but the accuracy varies for other properties. For most solute–solute and solute–vacancy interactions all interatomic potentials tend to capture the general trends though there is a disparity in the predicted magnitudes. While currently 'off-the-shelf' universal machine learning interatomic potentials fail to predict some key properties, some of them show significant potential to serve as starting point for further training and refinement.

In the present study, an atomistic K-test framework for the fracture toughness assessment of generally oriented grain boundaries (GBs) (tilt, twist, mixed) and triclinic single crystals is investigated. Boundary conditions for the modelling of cracks along the interfaces of bicrystals are derived based on the $6{\text{th}}$-order Stroh formalism and compared with established approaches. Thereby, especially the oscillations in the relevant field quantities due to mode I loading are critically assessed. It is found that for engineering applications, these oscillations do not compromise the validity of the employed approach since they are confined to a negligibly small region at the crack tip. Next, the $6{\text{th}}$-order Stroh approach is used to investigate crack propagation along GBs under mode I loading. A crack identification and crack tip tracing scheme is included to update the imposed boundary conditions during the simulations. Lastly, a physically-motivated method is discussed and incorporated, which allows for the unambiguous determination of the fracture toughness, including challenging setups such as generally oriented GBs. The so-established K-test setup is validated with a series of numerical examples.

The anisotropy of crystal structures mandates the direction dependence of materials' mechanical properties. Key properties of interest are the Young's modulus and Poisson ratio in the small strain limit, and the ideal tensile strength in the large strain regime. To date, atomistic computations of these properties have been conducted using two approaches. The first approach explicitly calculates the stress–strain response using computational tensile test experiments. The second approach computes the single crystal elastic constants then derives the mechanical properties using analytical equations. The two approaches have been used interchangeably and their equivalence not assessed. This work systematically computes the mechanical properties of 13 BCC and 12 face centered cubic (FCC) metals via the two approaches using first principles density functional theory calculations and hypothesize the robustness of the first approach. Analysis of the results has revealed the shortcomings of the elastic constants method in detecting instabilities in the structures captured by the first principles computational tensile test approach. Large discrepancies in calculations of Young's moduli using the latter approach are herein reported, as well as auxetic repossess and large Poisson ratio for some metals. Beyond the small strain results, we systematically examined the lateral strain response up to 0.5 applied strain in 3 crystallographic directions and reported large changes in slopes and peculiarities around the Bain transformation strain. From the computational stress–strain results, we validated empirical equations in the literature relating the ideal strength to the direction-dependent Young's modulus and the Bain strain along [001] in BCC and [110] in FCC but also presented further relations for other crystallographic directions. In conclusion, we believe that the elastic constants approach, while computationally efficient, has to be used with caution and should be validated against the computational tensile tests. In addition, we highlight the importance of examining different crystallographic directions with possibly desirable properties.

Theory predicts limiting gliding velocities that dislocations cannot overcome. Computational and recent experiments have shown that these limiting velocities are soft barriers and dislocations can reach transonic speeds in high rate plastic deformation scenarios. In this paper we systematically examine the mobility of edge and screw dislocations in several face centered cubic (FCC) metals (Al, Au, Pt, and Ni) in the extreme large-applied-stress regime using molecular dynamics simulations. Our results show that edge dislocations are more likely to move at transonic velocities due to their high mobility and lower limiting velocity than screw dislocations. Importantly, among the considered FCC metals, the dislocation core structure determines the dislocation's ability to reach transonic velocities. This is likely due to the variation in stacking fault width due to relativistic effects near the limiting velocities.

Over low and intermediate strain rates, plasticity in face centered cubic (FCC) metals is governed by the glide of dislocations, which manifest as complex networks that evolve with strain. Considering the elastic anisotropy of FCC metals, the characteristics of dislocation motion are also anisotropic (i.e. dislocation character angle-dependent), which is expected to notably influence the overall evolution of the dislocation network, and consequently, the plastic response of these materials. The aggregate influence of the anisotropy in the Peierls stress on the mechanical response of single crystal Ni was investigated in the present work using discrete dislocation dynamics simulations. Twenty initial dislocation networks, differing in their configuration and dislocation density, were deformed under uniaxial tension up to at least 0.9% strain, and the analysis of character-dependent dynamics showed a suppression of plasticity only for segments of nearly screw character. While the increased screw component of the Peierls stress raised the initial strain hardening rate, it also resulted in longer dislocation segments overall, contrary to the reasoning that longer pinned segments exhibit a lower resistance to motion and might give a weaker response. A non-linear superposition principle is demonstrated to predict the hardening reasonably well, considering the cumulative effects of forest and Peierls stress-related strengthening. Further analysis of the network topology revealed a tendency to maintain connectivity over the course of deformation for those networks simulated using an unequal Peierls stress. The general increases in hardening rate and network connectivity contrast with the localized reduction of dislocation motion, which occurred mainly for segments of nearly screw-type character.

An interatomic potential, traditionally regarded as a mathematical function, serves to depict atomic interactions within molecules or solids by expressing potential energy concerning atom positions. These potentials are pivotal in materials science and engineering, facilitating atomic-scale simulations, predictive material behavior, accelerated discovery, and property optimization. Notably, the landscape is evolving with machine learning transcending conventional mathematical models. Various machine learning-based interatomic potentials, such as artificial neural networks, kernel-based methods, deep learning, and physics-informed models, have emerged, each wielding unique strengths and limitations. These methods decode the intricate connection between atomic configurations and potential energies, offering advantages like precision, adaptability, insights, and seamless integration. The transformative potential of machine learning-based interatomic potentials looms large in materials science and engineering. They promise tailor-made materials discovery and optimized properties for specific applications. Yet, formidable challenges persist, encompassing data quality, computational demands, transferability, interpretability, and robustness. Tackling these hurdles is imperative for nurturing accurate, efficient, and dependable machine learning-based interatomic potentials primed for widespread adoption in materials science and engineering. This roadmap offers an appraisal of the current machine learning-based interatomic potential landscape, delineates the associated challenges, and envisages how progress in this domain can empower atomic-scale modeling of the composition-processing-microstructure-property relationship, underscoring its significance in materials science and engineering.

Dislocation plasticity in bcc metals at low temperatures and stresses is well known to be controlled by screw dislocation mobility. Screw dislocations move by the nucleation of kink-pairs on the dislocation line and is a relatively well studied phenomenon. However, how screw dislocation mobility is influenced by interfaces and surfaces has not been well-studied. To provide insight into the role interfaces play in screw dislocation mobility, this study proposes an empirical model to treat a grain boundary as a dislocation source, which is then implemented into kinetic Monte Carlo simulations for body-centered-cubic (bcc) metals. This effort focuses on the roles of kink nucleation and migration processes at the interface, comparing the energetics of these events on interfaces versus those on the interior of the dislocation line. The findings reveal that interfaces can either enhance dislocation motion or not affect it at all, depending on temperature, stress, and dislocation length. This work provides insights into the mesoscale behavior of bcc metals and bridges the gap between experimental observations and computational models at small scales.