arXiv subjects

Amin Esfandiarpour

Publications and source records attributed to Amin Esfandiarpour.

7 recordsLinked to original sources

arXiv2026

TorchNEP: Ultra-Efficient and Accurate Training of Neuroevolution Potentials

Neuroevolution Potential (NEP) is one of the most efficient machine-learned interatomic potential frameworks for large-scale atomistic simulations. However, its original training strategy remains computationally demanding, limiting systematic exploration of model architectures and training protocols. Here, we present TorchNEP, a PyTorch-based implementation of NEP that combines analytically derived gradients, adaptive optimization, and a two-stage training strategy. TorchNEP accelerates training by more than two orders of magnitude while maintaining full compatibility with existing NEP models. We further show that the improvement in predictive accuracy primarily originates from the two-stage training protocol rather than the optimization algorithm itself. Across diverse benchmark datasets, TorchNEP consistently improves force and stress predictions while maintaining comparable or improved energy accuracy. Benchmark evaluations on elemental and alloy systems demonstrate enhanced predictive performance for both atomic configurations and key materials properties. Furthermore, we show that increasing model complexity does not necessarily improve predictive performance despite reducing training errors. Overall, TorchNEP provides an efficient and flexible training framework for developing more accurate and robust machine-learned interatomic potentials.

physics.comp-ph↗

arXiv2026

General-Purpose Machine-Learned Potential for CrCoNi Alloys Enabling Large-Scale Atomistic Simulations with First-Principles Accuracy

CrCoNi medium-entropy alloys exhibit exceptional mechanical properties arising from pronounced chemical complexity, including short-range order (SRO), and low stacking fault energy, posing challenges for large-scale atomistic simulations. While most models focus on equimolar compositions, deviations from equimolarity provide an effective route to tuning properties, requiring transferable interatomic potentials that capture composition-dependent behavior. Here we develop a general-purpose machine-learned interatomic potential for the CrCoNi system within the neuroevolution potential (NEP) framework, achieving near first-principles accuracy with high computational efficiency. Trained on a comprehensive dataset spanning pure elements, binary and ternary alloys across a wide compositional range, diverse crystal structures and thermodynamic conditions, and based on spin-polarized \textit{ab initio} data, the model accurately reproduces equations of state, phonons, elastic constants, dislocation dissociation, surface and defect energies, melting temperatures and strain-induced phase transformations. It further captures SRO and its effect on stacking fault energies across both equimolar and non-equimolar compositions, in agreement with first-principles and experiments. In contrast to existing potentials, typically limited to equimolar alloys and less accurate for pure elements, the present model delivers consistent accuracy across the full compositional space while retaining superior efficiency. These results enable reliable atomistic simulations of composition-dependent behaviour and provide a framework for the design of non-equimolar CrCoNi alloys.

cond-mat.mtrl-sci↗

arXiv2025

Exploring the impact of Ti/Al on L12 nanoprecipitation and deformation behavior in CoNiFeAlTi multi-principal element alloys through atomistic simulations

Recent studies on CoNi-based multi-principal element alloys (MPEAs) have demonstrated high strength and ductility, attributed to the formation of stable L12 nanoscale precipitates. However, the fundamental mechanisms behind such impressive properties in these complex alloys are not well understood. In this work, we investigate the effects of Ti and Al concentrations on the formation of L12 precipitates in (CoNiFe)84(Al8Ti8), (CoNiFe)86(Al7Ti7), (CoNiFe)88(Al6Ti6), and (CoNiFe)94(Al4Ti2) MPEAs using hybrid molecular dynamics/Monte Carlo (MD/MC) simulations and a MEAM interatomic potential for the CoNiFeTiAl system. Additionally, we study the effect of L12 precipitation on the mechanical properties and stacking fault energy (SFE) of these MPEAs using MD. Our hybrid MD/MC simulations show that the (CoNiFe)86(Al7Ti7) alloy exhibits the highest amount of L12 nanoprecipitates. We find that L12 precipitation increases the SFE, with higher Al and Ti contents leading to greater increases. Tensile simulations reveal that L12 precipitates enhance yield strength, with alloys exhibiting higher precipitation showing increased flow stress. We also investigate dislocation-nanoprecipitate interactions with different precipitate sizes in the (CoNiFe)86(Al7Ti7) alloy. Larger nanoprecipitate sizes result in stronger dislocation pinning. Dislocations predominantly shear through 4-8 nm precipitates instead of looping around them (Orowan mechanism), enhancing strength while maintaining good ductility. Although the lattice mismatch between the L12 nanoprecipitate and the matrix is low (0.139%), the significant difference in SFE between the L12 nanoprecipitate and the matrix results in stronger dislocation pinning. This understanding can guide the design of MPEAs with tailored properties by controlling nanoscale precipitation.

cond-mat.mtrl-sci↗

arXiv2025

Comparative study on radiation resistance of WTaCrV high-entropy alloy and tungsten in helium-containing conditions

W and W-based high-entropy alloys (HEAs) are promising candidates for plasma-facing materials in fusion reactors. While irradiation studies on W have revealed a tendency for helium (He) bubble formation and radiation-induced defects, investigations of WTaCrV HEA have demonstrated superior radiation resistance, whether under He+ irradiation or heavy ion irradiation. To assess material performance under conditions relevant to fusion reactors - characterized by fast neutrons and gas production from transmutation reactions - complex irradiation environments need to be modeled. Using molecular dynamics simulations, we examined defect evolution in W and equimolar WTaCrV HEA with and without preexisting He atoms under cascade overlap conditions up to 0.2 dpa at 300 K. In W, dislocation loops and large interstitial clusters formed readily, with increasing He content leading to higher dislocation densities and the formation of polygonal interstitial networks. In contrast, the WTaCrV alloy exhibited strong resistance to the formation of dislocation loops and large interstitial clusters but was more susceptible to the formation of bubbles at higher He concentrations. Bubble growth was driven by helium trapping at vacancy sites and the coalescence of smaller bubbles. Larger bubbles remained stable against cascade overlap, limiting further growth by coalescence.

cond-mat.mtrl-sci↗

arXiv2023

Serrated plastic flow in slowly-deforming complex concentrated alloys: universal signatures of dislocation avalanches

Under plastic flow, multi-element high/medium-entropy alloys (HEAs/MEAs) commonly exhibit complex intermittent and collective dislocation dynamics owing to inherent lattice distortion and atomic-level chemical complexities. Using atomistic simulations, we report on an avalanche study of slowly-driven model face-centered cubic (fcc) NiCoCrFeMn and NiCoCr chemically complex alloys aiming for microstructural/topological characterization of associated dislocation avalanches. The results of our avalanche simulations reveal a close correspondence between the observed serration features in the stress response of the deforming HEA/MEA and the incurred slip patterns within the bulk crystal. We show that such correlations become quite pronounced within the rate-independent (quasi-static) regime exhibiting scale-free statistics and critical scaling features as universal signatures of dislocation avalanches.

cond-mat.mtrl-sci↗

arXiv2022

Dislocation plasticity in equiatomic NiCoCr alloys: The effect of short-range order

Equiatomic NiCoCr solid solutions have been recently shown to display exceptional mechanical properties, that have been suggested to be linked to nanostructural short-range order (SRO) features that may arise from thermal treatments, such as annealing or/and aging. Here, we use hybrid Monte Carlo-Molecular Dynamics simulations to gain further insights of thermal effects on the SRO formation as well as the edge dislocation plasticity mechanisms of equiatomic NiCoCr face-centered cubic solid solution. For that purpose, we utilize two well-known NiCoCr interatomic potentials, one of which displays well documented SRO, believed to be linked to experimental evidence and labeled as the Li-Sheng-Ma potential, while the other (Farkas-Caro) does not. We use these two potentials to discern short range ordering (from inherent randomness in random solid solutions) and understand how SROs influence dislocation depinning dynamics in various thermal annealing scenarios. In this context, we used robust, scale-dependent metrics to infer a characteristic SRO size in the Li-Sheng-Ma case by probing local concentration fluctuations which otherwise indicate uncorrelated patterns in the Farkas-Caro case in a close agreement with random alloys. Using relevant order parameters, we also report on the drastic increase of chemical ordering within the stacking fault region. More importantly, we find that the Li-Sheng-Ma potential leads to excellent edge dislocation depinning strength with low stacking fault width. Our findings indicate an enhanced roughening mechanism due to the SROs-misfit synergy that leads to significant improvements in dislocation glide resistance. We argue that the improvements in alloy strength have their atomistic origins in the interplay between nanoscopic SROs and atomic-level misfit properties.

cond-mat.mtrl-sci↗

arXiv2021

Shear banding instability in \remove{high entropy} multi-component metallic glasses: Interplay of composition and short-range order}

The shear-banding instability in quasi-statically driven bulk metallic glasses emerges from collective dynamics, mediated by shear transformation zones and associated non-local elastic interactions. It is also phenomenologically known that sharp structural features of shear bands are typically correlated to the sharpness of the plastic yielding transition, being predominant in commonly studied alloys composed of multiple different elements, that have very different atomic radii. However, in the opposite limit \remove{of high-entropy multicomponent alloys,} where elements' radii are relatively similar, plastic yielding of bulk metallic glasses is highly dependent on compositional and ordering features. In particular, a known mechanism at play involves the formation of short-range order dominated by icosahedra-based clusters. Here, we report on atomistic simulations of multi-component metallic glasses with different chemical compositions showing that the degree of strain localization is largely controlled by the interplay between composition-driven icosahedra-ordering and collectively-driven shear transformation zones. By altering compositions, strain localization ranges from diffuse homogenized patterns to singular crack-like features. We quantify the dynamical yielding transition by measuring the atoms' susceptibility to plastic rearrangements, strongly correlated to the local atomic structure. We find that the abundance of short-range ordering of icosahedra within rearranging zones increases glassy materials' capacity to delocalize strain. The kind of plastic yielding can be often qualitatively inferred by the commonly used compositional descriptor that characterizes element associations, the misfit parameter $δ_a$, and also by uncommon ones, such as shear-band width and shear-band dynamics' correlation parameters.

cond-mat.mtrl-sci↗