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Aayush Sharma

Publications and source records attributed to Aayush Sharma.

8 recordsLinked to original sources

Pseudoelastic deformation in Mo-based refractory multi-principal element alloys

Phase diagrams supported by density functional theory methods can be crucial for designing high-entropy alloys that are subset of multi-principal$-$element alloys. We present phase and property analysis of quinary (MoW)$_{x}$Zr$_{y}$(TaTi)$_{1-x-y}$ refractory high-entropy alloys from combined Calculation of Phase Diagram (CALPHAD) and density-functional theory results, supplemented by molecular dynamics simulations. Both CALPHAD and density-functional theory analysis of phase stability indicates a Mo-W-rich region of this quinary has a stable single-phase body-centered-cubic structure. We report first quinary composition from Mo$-$W$-$Ta$-$Ti$-$Zr family of alloy with pseudo-elastic behavior, i.e., hysteresis in stress$-$strain. Our analysis shows that only Mo$-$W$-$rich compositions of Mo$-$W$-$Ta$-$Ti$-$Zr, i.e., Mo$+$W$\ge$ 85 at.%, show reproducible hysteresis in stress-strain responsible for pseudo-elastic behavior. The (MoW)$_{85}$Zr$_{7.5}$(TaTi)$_{7.5}$ was down-selected based on temperature-dependent phase diagram analysis and molecular dynamics simulations predicted elastic behavior that reveals twinning assisted pseudoelastic behavior. While mostly unexplored in body-centered-cubic crystals, twinning is a fundamental deformation mechanism that competes against dislocation slip in crystalline solids. This alloy shows identical cyclic deformation characteristics during uniaxial $\lt$100$\gt$ loading, i.e., the pseudoelasticity is isotropic in loading direction. Additionally, a temperature increase from 77 to 1500 K enhances the elastic strain recovery in load-unload cycles, offering possibly control to tune the pseudoelastic behavior.

cond-mat.mtrl-sci

On the martensitic transformation in Fe$_{x}$Mn$_{80-x}$Co$_{10}$Cr$_{10}$ high-entropy alloy

High-entropy alloys (HEAs), and even medium-entropy alloys (MEAs), are an intriguing class of materials in that structure and property relations can be controlled via alloying and chemical disorder over wide ranges in the composition space. Employing density-functional theory combined with the coherent-potential approximation to average over all chemical configurations, we tune free energies between face-centered-cubic (fcc) and hexagonal-close-packed (hcp) phases in Fe$_{x}$Mn$_{80-x}$Co$_{10}$Cr$_{10}$ systems.~Within Fe-Mn-based alloys, we show that the martensitic transformation and chemical short-range order directly correlate with the fcc-hcp energy difference and stacking-fault energies, which are in quantitative agreement with recent experiments on a $x$=40~at.\% polycrystalline HEA/MEA. Our predictions are further confirmed by single-crystal measurements on a$x$=40at.\% using transmission-electron microscopy, selective-area diffraction, and electron-backscattered-diffraction mapping. The results herein offer an understanding of transformation-induced/twinning-induced plasticity (TRIP/TWIP) in this class of HEAs and a design guide for controlling the physics behind the TRIP effect at the electronic level.

cond-mat.mtrl-sci

Neural-networks model for force prediction in multi-principal-element alloys

Atomistic simulations can provide useful insights into the physical properties of multi-principal-element alloys. However, classical potentials mostly fail to capture key quantum (electronic-structure) effects. We present a deep 3D convolutional neural network (3D CNN) based framework combined with a voxelization technique to design interatomic potentials for chemically complex alloys. We highlight the performance of the 3D CNN model and its efficacy in computing potentials using the medium-entropy alloy TaNbMo. In order to provide insights into the effect of voxel resolution, we implemented two approaches based on the inner and outer bounding boxes. An efficient 3D CNN model, which is as accurate as the density-functional theory (DFT) approach, for calculating potentials will provide a promising schema for accurate atomistic simulations of structure and dynamics of general multi-principle element alloys.

cond-mat.mtrl-sci

Accelerating computational modeling and design of high-entropy alloys

With huge design spaces for unique chemical and mechanical properties, we remove a roadblock to computational design of {high-entropy alloys} using a metaheuristic hybrid Cuckoo Search (CS) for "on-the-fly" construction of Super-Cell Random APproximates (SCRAPs) having targeted atomic site and pair probabilities on arbitrary crystal lattices. Our hybrid-CS schema overcomes large, discrete combinatorial optimization by ultrafast global solutions that scale linearly in system size and strongly in parallel, e.g. a 4-element, 128-atom model [a $10^{73+}$ space] is found in seconds -- a reduction of 13,000+ over current strategies. With model-generation eliminated as a bottleneck, computational alloy design can be performed that is currently impossible or impractical. We showcase the method for real alloys with varying short-range order. Being problem-agnostic, our hybrid-CS schema offers numerous applications in diverse fields.

cond-mat.mtrl-sci

Tuning phase-stability and short-range order through Al-doping in (CoCrFeMn)100-xAlx high entropy alloys

For (CoCrFeMn)$_{100-x}$Al$_{x}$ high-entropy alloys, we investigate the phase evolution with increasing Al-content (0 $\le$ x $\le$ 20 at.%). From first-principles theory, the Al-doping drives the alloy structurally from FCC to BCC separated by a narrow two-phase region (FCC+BCC), which is well supported by our experiments. We highlight the effect of Al-doping on the formation enthalpy and electronic structure of (CoCrFeMn)$_{100-x}$Al$_{x}$ alloys. As chemical short-range order (SRO) in multicomponent alloys indicates the nascent local order (and entropy changes), as well as expected low-temperature ordering behavior, we use thermodynamic linear-response within density-functional theory to predict SRO and ordering transformation and temperatures inherent in (CoCrFeMn)$_{100-x}$Al$_{x}$. The predictions agree with our present experimental findings, and other reported ones.

cond-mat.mtrl-sci

3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys

The need for advanced materials has led to the development of complex, multi-component alloys or solid-solution alloys. These materials have shown exceptional properties like strength, toughness, ductility, electrical and electronic properties. Current development of such material systems are hindered by expensive experiments and computationally demanding first-principles simulations. Atomistic simulations can provide reasonable insights on properties in such material systems. However, the issue of designing robust potentials still exists. In this paper, we explore a deep convolutional neural-network based approach to develop the atomistic potential for such complex alloys to investigate materials for insights into controlling properties. In the present work, we propose a voxel representation of the atomic configuration of a cell and design a 3D convolutional neural network to learn the interaction of the atoms. Our results highlight the performance of the 3D convolutional neural network and its efficacy in machine-learning the atomistic potential. We also explore the role of voxel resolution and provide insights into the two bounding box methodologies implemented for voxelization.

cond-mat.mtrl-sci

Twinning-induced pseudoelastic behavior in (MoW)$_{85}$(TaTi)$_{7.5}$Zr$_{7.5}$

We provide a critical atomistic evidence of pseudoelastic behavior in complex solid-solution BCC Mo-W-Ta-Ti-Zr alloy. Prior to this work, only limited single-crystal BCC solids of pure metals and quaternary alloys have shown pseudoelastic behavior at low temperatures and high strain rates. The deformation mechanisms investigated using classical molecular simulations under tensile-compressive loading reveal temperature-dependent pseudoelastic behavior aided by twinning during the loading-unloading cycle. The pseudoelasticity is found to be independent of loading directions with identical cyclic deformation characteristics during uniaxial loading. Additionally, temperature variation from 77 to 1500 K enhances the elastic strain recovery in the alloy.

cond-mat.mtrl-sci

Design of high-strength refractory complex solid-solution alloys

Nickel-based superalloys and near-equiatomic high-entropy alloys containing Molybdenum are known for higher temperature strength and corrosion resistance. Yet, complex solid-solution alloys offer a huge design space to tune for optimal properties at slightly reduced entropy. For refractory Mo-W-Ta-Ti-Zr, we showcase KKR electronic-structure methods via the coherent-potential approximation to identify alloys over 5-dimensional design space with improved mechanical properties and necessary global (formation enthalpy) and local (short-range order) stability. Deformation is modeled with classical molecular dynamic simulations, validated from our first-principles data. We predict complex solid-solution alloys of improved stability with greatly enhanced modulus of elasticity ($3\times$ at 300 K) over near-equiatomic cases, as validated experimentally, and with higher moduli above 500~K over commercial alloys ($2.3\times$ at 2000 K). We also show that optimal complex solid-solution alloys are not described well by classical potentials due to critical electronic effects.

cond-mat.mtrl-sci