arXiv subjects

Somnath Bhowmick

Publications and source records attributed to Somnath Bhowmick.

At least 19 recordsLinked to original sources

arXiv2026

Linking Electronic Bonding and Short-range Order to Strength in $\alpha$-Titanium Alloys: A First-Principles Study

The development of accurate strength prediction models for titanium alloys is critical for advanced materials design. This study systematically examines how the mechanical properties of $\alpha$-Ti are affected by substitutional (X = Al, V, Mo) and interstitial (Y = H, C, N, O) alloying elements, with a focus on electronic bonding. Using density functional theory (DFT), we uncover the short-range ordering (SRO) of substitutional atoms and quantify their influence on the electronic bonding and mechanical behavior. The primary novelty of this work lies in developing a predictive model for tensile strength that goes beyond traditional empirical approaches. To quantify the contributions of individual solutes to strengthening, we use physically grounded quantum-chemical descriptors, such as the Integrated Crystal Orbital Hamilton Population (ICOHP), which is a direct measure of bond strength derived from first-principles calculations. The resulting formula quantitatively predicts the tensile strength of a wide range of $\alpha$-Ti alloys, demonstrating a significant advancement in the computational design of high-performance structural materials.

cond-mat.mtrl-sci↗

arXiv2026

Bridging Phase-Field Model and Deep Learning for Predicting 2D and 3D Microstructure Evolution in Ternary Alloys

We develop a hybrid framework that integrates a phase-field model (PFM) with an attention-enhanced deep learning (DL) architecture to study ternary spinodal dealloying, a sophisticated self-organization approach used to fabricate three-dimensional bicontinuous, hierarchical nanoporous materials. The study captures three distinct phase-separation mechanisms that emerge during the early stages of spinodal decomposition in both two and three dimensions. The DL workflow consists of three key components: (i) a dimensionality-reducing autoencoder that provides compact representations of high-resolution microstructure images (256x256x3), (ii) an attention-augmented convolutional long short-term memory (ConvLSTM) network that learns complex spatiotemporal correlations governing microstructure evolution, and (iii) a novel slice-by-slice strategy that enables extension of the model to three-dimensional systems (128x128x128x3). We further demonstrate a hybrid simulation strategy in which PFM accurately captures rapid early-stage microstructure evolution, while the DL model efficiently predicts late-stage coarsening dynamics. The trained DL model achieves remarkable predictive accuracy, maintaining fidelity up to 400 timesteps ahead and generalizing to compositions outside the training distribution. By bridging the physical fidelity of PFM with the computational efficiency of DL, this framework establishes a robust platform for predictive modeling of microstructure evolution in complex multicomponent systems.

cond-mat.mtrl-sci↗

arXiv2026

Physics Aware Representation Learning on Electronic Charge Density for Materials Property Prediction

The fundamental quantity governing the mechanical and thermodynamic properties of a crystalline solid is its electronic charge density. Yet, its direct use for the rapid prediction of materials properties remains challenging due to its high dimensionality. Here, we present a physics-informed deep learning framework that directly predicts mechanical and thermodynamic properties from the three-dimensional electronic charge density derived from density functional theory (DFT). The proposed approach first utilizes a three-dimensional convolutional autoencoder for unsupervised dimensionality reduction, compressing a high-resolution charge-density grid (128 x 128 x 128) into a compact latent representation (16 x 16 x 16 x 16) while preserving physically meaningful features, as confirmed by negligible reconstruction errors across diverse crystal systems. The compressed latent-space representation of charge density is then used by two different regression models for property prediction: Light Gradient Boosting Machine (LightGBM) and Attention-based 3D Convolutional Neural Networks (Att CNN), and their performance is compared. Combining composition-based descriptors (Material Agnostic Platform for Informatics and Exploration or MAGPIE) with electronic charge density data further improves the model accuracy. Using a dataset of about 6059 inorganic compounds spanning multiple crystal symmetries, the models achieve strong predictive performance for bulk modulus K (R2 = 0.94), Young's modulus E (R2 = 0.88), shear modulus G (R2 = 0.87), formation energy Eform (R2 = 0.96), and Debye temperature {\Theta} (R2 = 0.89). This work establishes electronic charge density as a transferable, physics-grounded descriptor for materials property prediction, requiring ~ 1/25 the computational resources of full-fledged DFT calculations.

cond-mat.mtrl-sci↗

arXiv2025

Multiscale Modeling of Abnormal Grain Growth: Role of Solute Segregation and Grain Boundary Character

Abnormal grain growth (AGG) influences the properties of polycrystalline materials; however, the underlying mechanisms, particularly the role of solute segregation at the grain boundary (GB), are difficult to quantify precisely. This study demonstrates a multiscale framework that integrates atomic-scale segregation energetics (using density functional theory) with mesoscale grain growth dynamics (using phase-field model) to investigate AGG, using $α$-Fe as an example system. Multisite segregation energies are calculated for symmetric tilt grain boundaries (STGBs) along the $\langle 110 \rangle$ axis for nine different solutes (Co, Cr, Mn, Mo, Nb, Ni, Ti, W, and V), encompassing three different types of coincident site lattice (CSL) boundaries: $\sum 3 (11\bar{2})$, $\sum 9 (\bar{2}21)$, and $\sum 3 (\bar{1}11)$. The model takes into account the effect of solute drag on GB mobility, estimated using a bulk solute concentration of 0.1 at\%. The results demonstrate that AGG originates due to GB anisotropy, the extent of which largely depends on the type of solute atom present. Such a complex dependence necessitates using a multiscale model to understand AGG comprehensively. In general, low-energy $Σ3$ boundaries are found to have higher mobility and show preferential growth for most of the solutes, other than Co. The study reveals how the distribution of GB types significantly influences AGG. When 10-30\% of the GBs are high-mobility type, crown-like morphologies are observed, leading to AGG. These findings underscore the critical role of GB chemistry and crystallography in governing AGG, and the model can be generalized to provide a predictive framework for controlling grain growth through strategic solute design in advanced alloys.

cond-mat.mtrl-sci↗

arXiv2025

High-Throughput Screening of 2D Photocatalyst Heterostructures with Suppressed Electron-Hole Recombination for Solar Water Splitting

Efficient and scalable photocatalysts for solar water splitting remain a critical challenge in renewable energy research. The work presents a high-throughput first-principles discovery of two-dimensional (2D) type-II van der Waals heterostructures (vdWHs) optimized for visible-light-driven photocatalytic water splitting. We screened 482 heterostructures constructed from 60 experimentally realizable 2D monolayers and identified 148 stable type-II vdWHs with spatially separated valence and conduction band edges, out of which 65 satisfy the thermodynamic redox conditions for water splitting over a broad pH range. Among these, the best two, MoTe2/Tl2O and MoSe2/WSe2, exhibit a high visible-light absorption coefficient exceeding 0.6X10^6 cm-1, resulting in a high power conversion efficiency of 2%. Quantum kinetic analysis of the hydrogen evolution reaction (HER) reveals nearly barrierless free energy profiles across multiple adsorption sites. Our study further reveals that intrinsic interlayer electric fields in these vdWHs drive directional charge separation, suppressing carrier recombination. Our results establish a design framework for using type-II 2D heterostructures as tunable and experimentally accessible 2D photocatalysts for efficient hydrogen production.

cond-mat.mtrl-sci↗

arXiv2025

Deep Learning-Driven Prediction of Microstructure Evolution via Latent Space Interpolation

Phase-field models accurately simulate microstructure evolution, but their dependence on solving complex differential equations makes them computationally expensive. This work achieves a significant acceleration via a novel deep learning-based framework, utilizing a Conditional Variational Autoencoder (CVAE) coupled with Cubic Spline Interpolation and Spherical Linear Interpolation (SLERP). We demonstrate the method for binary spinodal decomposition by predicting microstructure evolution for intermediate alloy compositions from a limited set of training compositions. First, using microstructures from phase-field simulations of binary spinodal decomposition, we train the CVAE, which learns compact latent representations that encode essential morphological features. Next, we use cubic spline interpolation in the latent space to predict microstructures for any unknown composition. Finally, SLERP ensures smooth morphological evolution with time that closely resembles coarsening. The predicted microstructures exhibit high visual and statistical similarity to phase-field simulations. This framework offers a scalable and efficient surrogate model for microstructure evolution, enabling accelerated materials design and composition optimization.

cond-mat.mtrl-sci↗

arXiv2025

Deep Learning Assisted Denoising of Experimental Micrographs

Microstructure imaging is crucial in materials science, but experimental images often introduce noise that obscures critical structural details. This study presents a novel deep learning approach for robust microstructure image denoising, combining phase-field simulations, Fourier transform techniques, and an attention-based neural network. The innovative framework addresses dataset limitations by synthetically generating training data by combining computational phase-field microstructures with experimental optical micrographs. The neural network architecture features an attention mechanism that dynamically focuses on important microstructural features while systematically eliminating noise types like scratches and surface imperfections. Testing on a FeMnNi alloy system demonstrated the model's exceptional performance across multiple magnifications. By successfully removing diverse noise patterns while maintaining grain boundary integrity, the research provides a generalizable deep-learning framework for microstructure image enhancement with broad applicability in materials science.

cond-mat.mtrl-sci↗

arXiv2025

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study

The generative adversarial network (GAN) is one of the most widely used deep generative models for synthesizing high-quality images with the same statistics as the training set. Finite element method (FEM) based property prediction often relies on synthetically generated microstructures. The phase-field model is a computational method of generating realistic microstructures considering the underlying thermodynamics and kinetics of the material. Due to the expensive nature of the simulations, it is not always feasible to use phase-field for synthetic microstructure generation. In this work, we train a GAN with microstructures generated from the phase-field simulations. Mechanical properties calculated using the finite element method on synthetic and actual phase field microstructures show excellent agreement. Since the GAN model generates thousands of images within seconds, it has the potential to improve the quality of synthetic microstructures needed for FEM calculations or any other applications requiring a large number of realistic synthetic images at minimal computational cost.

cond-mat.mtrl-sci↗

arXiv2024

Transistors based on Novel 2-D Monolayer Semiconductors Bi2O2Se, InSe, and MoSi2N4 for Enhanced Logic Density Scaling

Making ultra-short gate-length transistors significantly contributes to scaling the contacted gate pitch. This, in turn, plays a vital role in achieving smaller standard logic cells for enhanced logic density scaling. As we push the boundaries of miniaturization, it is intriguing to consider that the ultimate limit of contacted gate pitch could be reached with remarkable 1 nm gate-length transistors. Here, we identify InSe, Bi2O2Se, and MoSi2N4 as potential two-dimensional semiconductors for 1 nm transistors with low contact resistance and outstanding interface properties. We employ a fully self-consistent ballistic quantum transport model starting from first-principle calculations. Our simulations show that the interplay between electrostatics and quantum tunneling influences the performance of these devices over the device design space. MoSi2N4 channels have the best immunity to quantum tunneling, and Bi2O2Se channel devices have the best electrostatics. We show that for a channel length of 12 nm, all the devices can deliver I_$ON$/I_$OFF$ > 10^3 , suitable for electronic applications, and Bi2O2Se is the best-performing channel material.

cond-mat.mes-hall↗

arXiv2024

Harnessing Room-Temperature Ferroelectricity in Metal Oxide Monolayers for Advanced Logic Devices

Two-dimensional ferroelectric materials are beneficial for power-efficient memory devices and transistor applications. Here, we predict out-of-plane ferroelectricity in a new family of buckled metal oxide (MO; M: Ge, Sn, Pb) monolayers with significant spontaneous polarization. Additionally, these monolayers have a narrow valence band, which is energetically separated from the rest of the low-lying valence bands. Such a unique band structure limits the long thermal tail of the hot carriers, mitigating subthreshold thermionic leakage and allowing field-effect transistors (FETs) to function beyond the bounds imposed on conventional FETs by thermodynamics. Our quantum transport simulations reveal that the FETs based on these MO monolayers exhibit a large ON/OFF ratio with an average subthreshold swing of less than 60 mV/decade at room temperature, even for short gate lengths. Our work motivates further exploration of the MO monolayers for developing advanced, high-performance memory and logic devices.

physics.app-ph↗

arXiv2024

Grain boundary grooving in thin film under the influence of an external magnetic field: A phase-field study

Using a phase-field model, we study the surface diffusion-controlled grooving of a moving grain boundary under the influence of an external magnetic field in thin films of a nonmagnetic material. The driving force for the grain boundary motion comes from the anisotropic magnetic susceptibility of the material, leading to the free energy difference between differently oriented grains. We find that above a critical magnetic field the grain boundary motion is in a steady state, and under this condition, the mobile thermal groove exhibits a universal behavior scaled surface profiles are timeinvariant and independent of thermodynamic parameters. The simulated universal curve agrees well with Mullins theory of mobile grooves for any groove shape. We extend our study to a three-dimensional polycrystalline thin film with equalsized hexagonal grains. We observe a preferential grain growth depending on the applied magnetic field direction, which can be leveraged for field-assisted texture control of polycrystalline thin films. Our study reveals that keeping other conditions the same, the rate of pitting at the vertices of the hexagonal grains substantially decreases in the presence of the external magnetic field.

cond-mat.mes-hall↗

arXiv2024

Room Temperature Ferroelectricity and Electrically Tunable Berry Curvature Dipole in III-V Monolayers

Two-dimensional ferroelectric monolayers are promising candidates for compact memory devices and flexible electronics. Here, through first-principles calculations, we predict room temperature ferroelectricity in AB-type monolayers comprising group III (A = Al, In, Ga) and group V (B = As, P, Sb) elements. We show that their spontaneous polarization, oriented out-of-plane, ranges from 9.48 to 13.96 pC/m, outperforming most known 2D ferroelectric. We demonstrate electric field tunable Berry curvature dipole and nonlinear Hall current in these monolayers. Additionally, we highlight their applicability in next-generation memory devices by forming efficient ferroelectric tunnel junctions, especially in InP, which supports high tunneling electroresistance. Our findings motivate further exploration of these monolayers for studying the interplay between Berry curvature and ferroelectricity and for integrating these ferroelectric monolayers in next-generation electronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning

Stacking fault energies (SFEs) are vital parameters for understanding the deformation mechanisms in metals and alloys, with prior knowledge of SFEs from ab initio calculations being crucial for alloy design. Machine learning (ML) algorithms employed in the present work demonstrate approximately 80 times acceleration in predicting generalized stacking fault energy (GSFE), which is otherwise computationally expensive to obtain directly from density functional theory (DFT) calculations, particularly for alloys. The features used to train the ML algorithms stem from the physics-based Friedel model, revealing a connection between the physics of d-electrons and the deformation behavior of transition metals and alloys. Predictions based on the ML model are consistent with experimental data. This model could aid in accelerating alloy design by offering a rapid method for screening materials based on stacking fault energies.

cond-mat.mtrl-sci↗

arXiv2023

Kinetic study of the CN + C2H6 hydrogen abstraction reaction based on an analytical potential energy surface

Temperature dependence of the thermal rate constants and kinetic isotope effects (KIE) of the CN + C2H6 gas-phase hydrogen abstraction reaction was theoretically determined within the 25-1000 K temperature range, i.e., from ultra-low to high-temperature regimes. Based on a recently developed full-dimensional analytical potential energy surface fitted to highly accurate explicitly correlated ab initio calculations, three different kinetic theories were used: canonical variational transition state theory (CVT), quasiclassical trajectory theory (QCT), and ring polymer molecular dynamics (RPMD) method for the computation of rate constants. We found that the thermal rate constants obtained with the three theories show a V-shaped temperature dependence, with a pronounced minimum near 200 K, qualitatively reproducing the experimental measurements. Among the three methods used in this work, the QCT and RPMD methods have the best agreement with the experiment at low and high temperatures, respectively. The significant increase in the rate constant at ultra-low temperatures in this very exothermic and practically barrierless reaction can be attributed to the large value of the impact parameter, ruling out the role of the tunneling effect and the intermediate complexes in the entrance channel. The theoretical H/D KIE depicted a normal behaviour, i.e., values greater than unity, emulating the experimental measurements and previous theoretical results. Finally, the discrepancies between theory and experiments were analysed as a function of several factors, such as limitations of the kinetics theories and the potential energy surface, as well as the uncertainties in the experimental measurements.

physics.chem-ph↗

arXiv2023

Room Temperature Ferroelectricity, Ferromagnetism, and Anomalous Hall Effect in Half-metallic Monolayer CrTe

Two-dimensional materials hosting ferroelectricity and ferromagnetism are crucial for low-power and high-speed information processing technologies. However, intrinsic 2D multiferroics in the monolayer limit are rare. Here, we demonstrate that monolayer CrTe, obtained by cleaving the [002] surface, is dynamically stable multiferroic at temperatures beyond room temperature. We show that it orders ferromagnetically with significant in-plane magnetocrystalline anisotropy, and it is a half-metal featuring a large half-metal gap. Remarkably, the broken inversion symmetry and buckled geometry of monolayer CrTe make it a ferroelectric with a large spontaneous out-of-plane polarization and significant magnetoelectric coupling. In addition, we demonstrate polarization or electric field-induced tunability of the anomalous Hall effect, accompanied by substantial bandstructure modulation. Our findings establish monolayer CrTe as a room-temperature multiferroic with great potential for applications in spintronics and ferroelectric devices.

cond-mat.mes-hall↗

arXiv2023

Microstructure-property prediction of a Ni-based superalloy: A combined phase-field and finite element modelling approach

Multiscale modelling is a new paradigm that has emerged in recent times to study the well-known problem of the process-structure-property relationship in the area of materials science and engineering. For obtaining the desired performance for materials of strategic importance, such as superalloys, it is essential to bridge different length and time scales in order to navigate the entire design space. In the present study, we develop a physics-based model for a Ni-based superalloy where the microstructures simulated using a phase-field model serve as input to finite-element computations. We examine the alloy's microstructure evolution and effective elastic properties quantitatively via phase-field and finite element methods integrated with CALPHAD database, by varying composition and aging temperature. The phase-field simulations provide us with an insight into the different regimes of microstructure evolution. The finite element analysis uncovers the relation of effective elastic properties with several system parameters.

cond-mat.mtrl-sci↗

arXiv2023

$μ$2mech: a Software Package Combining Microstructure Modeling and Mechanical Property Prediction

We have developed a graphical user interface (GUI) based package $μ$2mech to perform phase-field simulation for predicting microstructure evolution. The package can take inputs from ab initio calculations and CALPHAD (Calculation of Phase Diagrams) tools for quantitative microstructure prediction. The package also provides a seamless connection to transfer output from the mesoscale phase field method to the microscale finite element analysis for mechanical property prediction. Such a multiscale simulation package can facilitate microstructure-property correlation, one of the cornerstones in accelerated materials development within the integrated computational materials engineering (ICME) framework.

cond-mat.mtrl-sci↗

arXiv2023

Versatility of type-II van der Waals heterostructures: a case study with SiH-CdCl2

Unlike bilayers or a few layers thick materials, heterostructures are designer materials formed by assembling different monolayers in any desired sequence. As a result, while multilayer materials come with their intrinsic properties, heterostructures can be tailor-made to suit specific applications. Taking SiH-CdCl 2 as a representative system, we show the potential of heterostructures for several applications, like piezoelectricity, photocatalytic water splitting, and tunnel field effect transistor (TFET). Our study confirms that the characteristics of the heterostructure mainly depend on the potential difference between the constituent monolayers. From the vast database of available layered materials, many such combinations with a suitable potential difference are expected to have similar properties. Our work points to a vast pool of assembled materials with multifunctionality, an excellent asset for next-generation device applications.

cond-mat.mtrl-sci↗