arXiv subjects

Tanvir Sohail

Publications and source records attributed to Tanvir Sohail.

3 recordsLinked to original sources

arXiv2026

Permutation invariant neural network prediction of vacancy formation under deformation and varying chemical environment in FCC high entropy alloys

Vacancy formation energies govern diffusion, irradiation damage, phase stability, and dynamic failure in high-entropy alloys (HEAs), yet their strong dependence on local chemical environments and mechanical deformation makes atomistic calculations prohibitively expensive for large-scale studies. Here, we develop an atomistically informed permutation invariant machine learning framework for predicting strain dependent vacancy formation energies in FCC HEAs from local atomic environments. The model employs a vacancy-centered representation constructed from objective geometric descriptors together with invariants of the local deformation gradient, enabling the coupled effects of chemical disorder and finite deformation to be learned within a unified framework. Atomistic simulations reveal that volumetric deformation is the dominant factor controlling the average variation in vacancy formation energy, whereas shear deformation has a comparatively minor influence. At the same time, substantial site to site variability persists under identical macroscopic loading, demonstrating that local chemical environments govern the statistical distribution of vacancy energetics beyond species-averaged trends. The proposed framework accurately predicts vacancy formation energies across diverse deformation states while providing orders-of-magnitude faster evaluation than direct atomistic simulations. These results establish an efficient route for incorporating stress-dependent defect energetics into multiscale models of diffusion, irradiation damage, and dynamic failure in chemically complex alloys.

cond-mat.mtrl-sci↗

arXiv2026

Neural operator accelerated atomistic to continuum concurrent multiscale simulations of viscoelasticity

We present a neural-operator-accelerated concurrent multiscale framework that couples atomistic simulations with continuum finite-element analysis for history-dependent materials, thereby making atomistic-continuum multiscale simulations of viscoelastic materials tractable. The approach replaces direct molecular dynamics (MD) evaluation of the constitutive response with a Recurrent Neural Operator (RNO) surrogate trained on atomistic simulations. The surrogate learns the strain-history-to-stress operator from molecular dynamics simulations and provides a discretization-independent approximation of the atomistic constitutive mapping, enabling efficient evaluation of stresses and latent internal variables at each quadrature point. The framework is implemented within an explicit finite-element solver, where the constitutive update reduces to inexpensive operator evaluations rather than repeated MD solves. Memory effects are represented through learned internal states, and transfer learning across temperature enables the surrogate to capture thermally dependent viscoelastic behavior. The method is assessed using polyurea through cyclic loading, Taylor impact, and plate impact simulations and compared with an experimentally calibrated viscoelastic polyurea model and a Johnson-Cook model. The neural-operator surrogate reproduces correct viscoelastic response while enabling atomistically informed dynamic simulations at scales that are not tractable with direct MD-FEM coupling.

cond-mat.mtrl-sci↗

arXiv2026

Quantum solver for single-impurity Anderson models with particle-hole symmetry

Quantum embedding methods, such as dynamical mean-field theory (DMFT), provide a powerful framework for investigating strongly correlated materials. A central computational bottleneck in DMFT is in solving the Anderson impurity model (AIM), whose exact solution is classically intractable for large bath sizes. In this work, we develop and benchmark a quantum-classical hybrid solver tailored for DMFT applications, using the variational quantum eigensolver (VQE) to prepare the ground state of the AIM with shallow quantum circuits. The solver uses a unified ansatz framework to prepare the particle and hole excitations of the ground-state from parameter-shifted circuits, enabling the reconstruction of the impurity Green's function through a continued-fraction expansion. We evaluate the performance of this approach across a few bath sizes and interaction strengths under noisy, shot-limited conditions. We compare three optimization routines (COBYLA, Adam, and L-BFGS-B) in terms of convergence and fidelity, assess the benefits of estimating a quantum-computed moment (QCM) correction to the variational energies, and benchmark the approach by comparing the reconstructed density of states (DOS) against that obtained using a classical pipeline. Our results demonstrate the feasibility of Green's function reconstruction on near-term devices and establish practical benchmarks for quantum impurity solvers embedded within self-consistent DMFT loops.

quant-ph↗