arXiv subjects

Avik Mahata

Publications and source records attributed to Avik Mahata.

8 recordsLinked to original sources

arXiv2026

First Principles Thermodynamics of Zr B Segregation at Grain Boundaries in Recycled Nd2Fe14B

Grain-boundary chemistry is central to the coercivity and thermal stability of Nd2Fe14B permanent magnets, particularly in recycled magnets where recovery of the hard-magnetic phase does not by itself restore the intergranular microstructure. Our recent experimental study showed that nanoscale ZrB2 precipitates can emerge at grain boundaries and triple junctions during recycling despite an overall Zr concentration of only about 0.1 at.%, where they are associated with boundary stabilization and suppression of grain coarsening. Here we use spin-polarized density-functional theory with a Hubbard correction (DFT+U) to determine the atomistic thermodynamics underlying this preferential localization. A systematic set of composition-matched bulk/grain-boundary DFT+U supercells provides a common correlated-electron description across boundary chemistries. We compare B, Zr, Dy, and ZrB2-like local configurations in bulk and grain-boundary environments of Nd2Fe14B. Excess B is strongly stabilized at the boundary, while Zr also shows an independent thermodynamic preference for the interfacial region. When Zr and B are combined in a ZrB2-like configuration, the boundary preference is retained, indicating that the interface remains favorable as Zr-B coordination develops. These results establish a thermodynamic pathway for the co-localization of Zr and B prior to ZrB2 formation. Magnetic-state analysis further shows that all compared structures remain within the same high-moment Fe-sublattice regime, and boundary-localized defects generally perturb the normalized magnetization less than their matrix counterparts. The calculations therefore provide a first-principles explanation for why Zr-B chemistry concentrates at intergranular regions and how such boundary-localized states can support the microstructural stability required for high-coercivity recycled Nd-Fe-B magnets.

cond-mat.mtrl-sci↗

arXiv2026

Interfacial Accommodation as a Candidate Ductility Pathway in Intermetallic-Rich Alloys

Heterophase interfaces are increasingly recognized as active participants in plastic deformation, yet quantitative methods for comparing their strain-accommodation capacity remain limited. Here, we present an atomistic framework for quantifying interface-mediated strain accommodation using molecular dynamics simulations. Interface broadening, roughening, and migration are extracted from atomistic trajectories and combined into an Interface Accommodation Index, with a normalized counterpart accounting for differences in initial interface structure. A weighting sensitivity analysis demonstrates that the relative ranking of interfaces is robust to the specific form of the index. The framework is demonstrated for three experimentally motivated interfaces, Al/Al3Ti, Al/Al9M2, and Al9M2/Al3Ti (M = Fe, Co, Ni), under tensile, compressive, and shear loading. Bulk simulations show that Shockley partial dislocations dominate plastic deformation, with alloy chemistry governing the transition toward mixed-character dislocation networks. Tensile loading produces the greatest interface accommodation, while shear produces comparatively limited structural evolution. The Al9M2/Al3Ti interface exhibits both the highest yield resistance and the largest accommodation response, suggesting that intermetallic-intermetallic interfaces can simultaneously sustain load and redistribute strain. These simulations quantify structural accommodation rather than ductility or fracture directly; their connection to macroscopic ductility therefore requires experimental validation. The framework provides a transferable approach for comparing interface accommodation in multiphase alloys and identifies interface chemistry and crystallography as important design variables for damage-tolerant structural materials.

cond-mat.mtrl-sci↗

arXiv2026

Atomistic Indicators of the Ductile-to-Brittle Transition in Polycrystalline Tungsten: Temperature and Rhenium Effects on Crack-Tip Plasticity

The fracture response of body-centered-cubic tungsten is governed by competition between crack-tip instability and dislocation-mediated plastic accommodation. Molecular dynamics (MD) simulations are used to examine edge-cracked polycrystalline W and W-Re under displacement-controlled Mode-I tension from 300-1800 K at a strain rate of 5 x 10^8 s^-1. Heating pure W reduces the instability stress, effective specimen stiffness, and pre-instability volumetric work density, while the instability strain remains comparatively scattered. A normalized work-loss metric derived from the pre-instability work gives a broad sigmoid crossover marker, T*_MD ~ 870 K, characteristic of the simulated geometry and high-rate loading conditions. Among the available W-Re compositions, W-10Re shows greater pre-instability deformation and work accumulation than pure W across most temperatures, while the maximum-stress response remains strongly temperature- and composition-dependent. Changes in retained dislocation character and near-tip activity indicate that Re modifies the crack-tip plastic-accommodation pathway rather than producing a simple strength increase. Common-state analysis further shows that Re alters the retained 1/2<111> line character and the temporal distribution of near-tip dislocation activity without uniformly increasing the local dislocation population. These coupled mechanical and defect-structure changes provide atomistic indicators of a high-rate ductile-to-brittle transition (DBT), but not a direct prediction of experimental DBTT. A separate OpenDiS/pydis calculation demonstrates source-like bow-out and heterogeneous dislocation-network development in crack-free polycrystalline W, providing mesoscale context without implying direct quantitative MD-DDD coupling.

cond-mat.mtrl-sci↗

arXiv2026

Ductility Design Rules for Tungsten based Refractory High Entropy Alloys from Sparse Experimental Datasets

Tungsten-based refractory high-entropy alloys (RHEAs) are promising materials for fusion applications but often remain brittle at room temperature because of tungsten's high ductile-to-brittle transition temperature (DBTT). To identify alloying strategies that improve ductility, we compiled a curated dataset of experimentally reported tungsten-containing alloys and developed composition-based, physics-informed descriptors for machine learning. Three classifiers were evaluated using nested cross-validation, and a support vector classifier (SVC) showed the best generalization for this sparse dataset. Shapley additive explanations identified the exchange-correlation parameter, valence electron concentration, pressure field, and electronegativity mismatch as the most influential features governing ductile-brittle behavior. Synthetic compositions generated within the convex interpolation domain of the training data were evaluated and visualized on pseudo-ternary diagrams to map predicted ductility trends. The model predicts that moderate additions of Ti, Ni, and Co increase the likelihood of room-temperature ductility, whereas high Cr contents promote brittle behavior. These predictions agree with published experimental observations, including Ti-assisted ductility through solid-solution and electronic effects and embrittlement in Cr-rich refractory alloys. Agreement between DFT-derived elastic descriptors and SVC decision-function margins further supports the physical relevance of the learned classification boundary. Although the available mechanical dataset remains limited and heterogeneous, the model captures experimentally consistent trends and provides an interpretable, physics-informed framework for screening tungsten-based RHEAs for targeted simulation and experimental validation in fusion environments.

cond-mat.mtrl-sci↗

arXiv2026

Predicting Coherent B2 Stability in Ru-Containing Refractory Alloys Through Thermodynamic Elastic Design Maps

Ruthenium-based B2 intermetallics are promising for refractory superalloys but are limited by the trade-off between high thermodynamic stability and elastic precipitation strain. We present a physics-guided machine learning framework integrating high-throughput Density Functional Theory (DFT), Random Forest screening, and Symbolic Regression to navigate this design space. This approach resolves the paradox where stoichiometric compounds like RuHf fail to achieve theoretical solvus temperatures. By deriving a closed-form physical law, we quantify the strain penalty: a 1% lattice misfit reduces the solvus temperature by approximately 200 degrees C. This finding confirms that maximizing thermodynamic driving force alone is insufficient. We demonstrate that multi-component alloying is structurally necessary, identifying ternary additions such as Al and Ti as essential lattice-tuning agents that zero out the elastic penalty. This framework establishes a rigorous, constraint-based protocol for alloy design, enabling the precise engineering of zero-misfit, high-stability microstructures.

cond-mat.mtrl-sci↗

arXiv2025

Toward Generalizable Surrogate Models for Molecular Dynamics via Graph Neural Networks

We present a graph neural network (GNN) based surrogate framework for molecular dynamics simulations that directly predicts atomic displacements and learns the underlying evolution operator of an atomistic system. Unlike conventional molecular dynamics, which relies on repeated force evaluations and numerical time integration, the proposed surrogate model propagates atomic configurations forward in time without explicit force computation. The approach represents atomic environments as graphs and combines message-passing layers with attention mechanisms to capture local coordination and many-body interactions in metallic systems. Trained on classical molecular dynamics trajectories of bulk aluminum, the surrogate achieves sub angstrom level accuracy within the training horizon and exhibits stable behavior during short- to mid-horizon temporal extrapolation. Structural and dynamical fidelity are validated through agreement with reference radial distribution functions and mean squared displacement trends, demonstrating that the model preserves key physical signatures beyond pointwise coordinate accuracy. These results establish GNN-based surrogate integrators as a promising and computationally efficient complement to traditional molecular dynamics for accelerated atomistic simulations within a validated regime.

cond-mat.mtrl-sci↗

arXiv2025

Development and Validation of Interatomic Potential for Sc and Al-Sc Alloys: Thermodynamics, Solidification, and Intermetallic Ordering

We present a second-nearest-neighbor Modified Embedded Atom Method (2NN--MEAM) potential for Scandium (Sc) and Aluminum-Scandium (Al--Sc) alloys that unifies cohesive, thermodynamic, and solidification behavior within a single transferable framework. The Sc component accurately reproduces cohesive energy, lattice constants, defect energetics, and the experimental melting point obtained from two-phase coexistence, demonstrating reliable description of both hcp and liquid phases. The Al--Sc binary interaction parameters were fitted using the L1$_2$--Al$_3$Sc reference and benchmarked against first-principles and calorimetric data. The potential reproduces the strong negative formation enthalpy of Al$_3$Sc (--0.45~eV~atom$^{-1}$), correct relative stability of competing phases, and realistic elastic properties. Mixing enthalpies of the liquid alloy agree with ideal-associated-solution and CALPHAD models, confirming that the potential captures exothermic Al--Sc association in the melt. Molecular-dynamics simulations of solidification reveal the expected temperature and composition dependence of homogeneous nucleation. Pure Al crystallizes readily, while Al--1~at.\%~Sc exhibits a longer incubation and slower growth at the same absolute temperature due to reduced undercooling and solute drag. Within the alloy, ordered Al$_3$Sc-type L1$_2$ embryos appear spontaneously, with Sc atoms occupying cube-corner (B) sites surrounded by twelve Al neighbors. Energy--volume trajectories confirm that the potential links thermodynamics to microstructural evolution. Overall, the developed 2NN--MEAM potential provides a quantitatively grounded basis for modeling melting, solidification, and intermetallic ordering in Sc and Al--Sc systems, enabling future multicomponent alloy design and large-scale nucleation studies.

cond-mat.mtrl-sci↗

arXiv2017

Understanding Homogeneous Nucleation in Solidification of Aluminum by Molecular Dynamics Simulations

Homogeneous nucleation from aluminum (Al) melt was investigated by million-atom molecular dynamics (MD) simulations utilizing the second nearest neighbor modified embedded atom method (MEAM) potentials. The natural spontaneous homogenous nucleation from the Al melt was produced without any influence of pressure, free surface effects and impurities. Initially isothermal crystal nucleation from undercooled melt was studied at different constant temperatures, and later superheated Al melt was quenched with different cooling rates. The crystal structure of nuclei, critical nucleus size, critical temperature for homogenous nucleation, induction time, and nucleation rate were determined. The quenching simulations clearly revealed three temperature regimes: sub-critical nucleation, super-critical nucleation, and solid-state grain growth regimes. The main crystalline phase was identified as face-centered cubic (fcc), but a hexagonal close-packed (hcp) and an amorphous solid phase were also detected. The hcp phase was created due to the formation of stacking faults during solidification of Al melt. By slowing down the cooling rate, the volume fraction of hcp and amorphous phases decreased. After the box was completely solid, grain growth was simulated and the grain growth exponent was determined for different annealing temperatures.

cond-mat.mtrl-sci↗