arXiv subjects

Sayan Bhowmik

Publications and source records attributed to Sayan Bhowmik.

6 recordsLinked to original sources

arXiv2026

Bulk Boundary Condition for Surface Calculations in Density Functional Theory

We present a bulk boundary condition formalism for surface calculations in Kohn--Sham density functional theory. The approach exploits the nearsightedness of electronic interactions in real space to restrict the calculation to a localized surface region. Within this region, the electron density is evaluated by leveraging the decay of the density matrix, with bulk values imposed on the density and electrostatic potential in the interior, and the electrostatic potential solved subject to bulk boundary conditions. The energy and atomic forces are computed using density-matrix-based expressions. Through representative calculations of surface and adsorption energies, we demonstrate the accuracy and efficiency of the proposed formalism.

cond-mat.mtrl-sci↗

arXiv2026

Electronic manifolds for extrapolative alloy discovery

This study presents a computationally efficient framework for accelerated alloy discovery that uses the non-interacting electron density to capture intrinsic structure-property relationships in refractory high-entropy alloys (HEAs). Unlike state-of-the-art approaches relying on expensive, self-consistent density functional theory calculations, our method employs the non-interacting electron density as the primary structural descriptor. By extracting physical features through directionally resolved two-point spatial correlations and compressing them via Principal Component Analysis, we efficiently map the design space. Coupling these descriptors with Bayesian active learning, we achieve a normalized mean absolute error (NMAE) of <2% for the bulk modulus of Al-Nb-Ti-Zr alloys using only 10 training samples (<0.2% of the dataset). Furthermore, we demonstrate that the model learns an electronic packing manifold that is transferable within the refractory BCC alloy family. Validated on a distinct 7-component refractory system (Mo-Nb-Ta-Ti-V-W-Zr) containing four elements entirely absent from the training data, the framework enables zero-shot transfer within the refractory BCC alloy class. Moreover, by augmenting the base model with just 20 samples from the target domain, we achieve high-fidelity predictions (NMAE<3%) for 7-component alloys, reducing data acquisition costs by orders of magnitude compared to standard workflows. A controlled comparison confirms that composition-based descriptors under the identical pipeline do not reach the same accuracy threshold within the same sample budget, establishing that the spatial autocorrelation encoding of the non-interacting electron density provides information beyond elemental composition statistics alone.

cond-mat.mtrl-sci↗

arXiv2025

Real-space Hubbard-corrected density functional theory

We present an accurate and efficient framework for real-space Hubbard-corrected density functional theory. In particular, we obtain expressions for the energy, atomic forces, and stress tensor suitable for real-space finite-difference discretization, and develop a large-scale parallel implementation. We verify the accuracy of the formalism through comparisons with established planewave results. We demonstrate that the implementation is highly efficient and scalable, outperforming established planewave codes by more than an order of magnitude in minimum time to solution, with increasing advantages as the system size and/or number of processors is increased. We apply this framework to examine the impact of exchange-correlation inconsistency in local atomic orbital generation and introduce a scheme for optimizing the Hubbard parameter based on hybrid functionals, both while studying TiO$_2$ polymorphs.

physics.comp-ph↗

arXiv2025

Ab initio study of strain-driven vacancy clustering in aluminum

We present a first principles investigation of strain-driven vacancy clustering in aluminum. Specifically, we perform Kohn-Sham density functional theory calculations to study the influence of hydrostatic strains on clustering in tri-, quad-, and heptavacancies. We find that compressive strains are a key driving force for vacancy aggregation, particularly for collapse of clusters on the (111) plane, consistent with prior experimental observations of vacancy clusters on this plane. Notably, we find that the heptavacancy on the (111) plane collapses to form a prismatic dislocation loop for hydrostatic compressive strains exceeding 5\%, highlighting the critical role of such strains in prismatic dislocation loop nucleation in aluminum.

cond-mat.mtrl-sci↗

arXiv2025

A Bayesian latent Gaussian conditional autoregressive copula model for analyzing spatially-varying trends in rainfall

Assessing the availability of rainfall water plays a crucial role in rainfed agriculture. Given the substantial proportion of agricultural practices in India being rainfed and considering the potential trends in rainfall amounts across years due to climate change, we build a statistical model for analyzing monsoon total rainfall data for 34 meteorological subdivisions of mainland India available for 1951-2014. Here, we model the marginal distributions using a gamma regression model and the dependence through a Gaussian conditional autoregressive (CAR) copula model. Due to the natural variation in the monsoon total rainfall received across various dry through wet regions of the country, we allow the parameters of the marginal distributions to be spatially varying, under a latent Gaussian model framework. The neighborhood structure of the regions determines the dependence structure of both the likelihood and the prior layers, where we explore both CAR and intrinsic CAR structures for the priors. The proposed methodology also effectively imputes the missing data. We use the Markov chain Monte Carlo algorithms to draw Bayesian inferences. In simulation studies, the proposed model outperforms several competitors that do not allow a dependence structure at the data or prior layers. Implementing the proposed method for the Indian areal rainfall dataset, we draw inferences about the model parameters and discuss the potential effect of climate change on rainfall across India. While the assessment of the impact of climate change on rainfall motivates our study, the proposed methodology can be easily adapted to other contexts dealing with non-Gaussian non-stationary areal datasets where data from single or multiple temporal covariates are also available, and it is appropriate to assume their coefficients to be spatially varying.

stat.AP↗

arXiv2024

Spectral scheme for atomic structure calculations in density functional theory

We present a spectral scheme for atomic structure calculations in pseudopotential Kohn-Sham density functional theory. In particular, after applying an exponential transformation of the radial coordinates, we employ global polynomial interpolation on a Chebyshev grid, with derivative operators approximated using the Chebyshev differentiation matrix, and integrations using Clenshaw-Curtis quadrature. We demonstrate the accuracy and efficiency of the scheme through spin-polarized and unpolarized calculations for representative atoms, while considering local, semilocal, and hybrid exchange-correlation functionals. In particular, we find that $\mathcal{O}$(200) grid points are sufficient to achieve an accuracy of 1 microhartree in the eigenvalues for optimized norm conserving Vanderbilt pseudopotentials spanning the periodic table from atomic number $Z = 1$ to $83$.

physics.comp-ph↗