arXiv subjects

Sourav Mal

Publications and source records attributed to Sourav Mal.

7 recordsLinked to original sources

arXiv2026

Property-Guided Diffusion for Inverse Design of Crystalline Materials

Diffusion-based generative models with property guidance have emerged as a promising paradigm for inverse materials design by enabling the generation of crystalline materials with user-specified target properties. However, despite recent advances, the effectiveness of property guidance, its influence on crystallographic symmetry, and the physical viability of generated materials remain poorly understood. To address these questions, we develop a property-guided framework based on the lightweight diffusion model DiffCrysGen using parameter-efficient adapter fine-tuning and classifier-free guidance (CFG). The resulting framework enables efficient multi-property crystal generation while preserving the knowledge learned during unconditional pre-training. Using formation energy together with saturation magnetization and Vickers hardness as representative inverse-design tasks, we systematically investigate the influence of CFG across a broad range of guidance strengths. Increasing the guidance scale progressively steers the generated property distributions toward the prescribed targets while reducing the fraction of lowest-symmetry ($P1$) structures and increasing the proportion of higher-symmetry structures. To evaluate physical viability, generated structures are geometrically prescreened and subsequently validated using a machine-learning interatomic potential (MLIP)-based workflow comprising structural relaxation and thermodynamic, dynamical, and property-specific analyses. The framework identifies thermodynamically and dynamically stable magnetic and mechanically hard materials with overall success rates of 12.3\% and 3.9\%, respectively. These results establish property-guided DiffCrysGen as an efficient framework for inverse materials design while providing new insights into the role of classifier-free guidance in crystal generation.

cond-mat.mtrl-sci↗

arXiv2026

mCGCNN: A Dual-Stream Crystal Graph Convolutional Neural Network for the Efficient Prediction of Magnetic Properties of Crystalline Materials

Magnetic order in crystals is governed by moment-carrying sublattices and ligand-mediated exchange pathways, yet standard crystal graph neural networks treat all atoms homogeneously and encode bonds primarily through pair distances. We propose mCGCNN, a magnetism-aware crystal graph network that augments the full structural graph with a dedicated magnetic subgraph. The magnetic stream performs angle-aware message passing over magnetic centers using metal-ligand-metal exchange-path descriptors motivated by Goodenough-Kanamori-Anderson physics, while layer-wise cross-coupling transfers structural and ligand-field information from the full crystal graph. A separate magnetic-sublattice pooling operation prevents the magnetic interaction from being diluted by nonmagnetic atoms. Benchmarked on a curated Materials Project spin-polarized DFT data, mCGCNN improves total magnetic moment prediction from a CGCNN test MAE of 2.54~$\mu_B$ to 2.02~$\mu_B$, outperforming a strengthened CGCNN readout baseline and raising the test $R^2$ from 0.644 to 0.776. When pretrained on moment regression, the same magnetic representation improves ferromagnetic/antiferromagnetic classification. The results demonstrate that incorporating exchange geometry directly into graph architectures provides a physically grounded route to predictive models of magnetic materials.

cond-mat.mtrl-sci↗

arXiv2025

DiffCrysGen: A Generative Diffusion Model for Accelerated Design of Inorganic Crystalline Materials

Efficient exploration of the vast chemical space is a fundamental challenge in materials design and discovery, particularly for designing functional inorganic crystalline materials with targeted properties. Diffusion-based generative models have emerged as a powerful route, but most existing approaches require domain-specific constraints and separate diffusion processes for atom types, atomic positions, and lattice parameters, adding complexity and limiting efficiency. Here, we present DiffCrysGen, a fully data-driven, score-based diffusion model that generates complete crystal structures in a single, end-to-end diffusion process. This unified framework simplifies the model architecture and accelerates sampling by two to three orders of magnitude compared to existing methods without compromising chemical and structural diversity of the generated materials. In order to demonstrate the efficacy of DiffCrysGen in generating valid and useful materials, using density functional theory (DFT), we validate a number of newly generated rare earth-free magnetic materials that are energetically and dynamically stable, and are potentially synthesizable. These include ferromagnets with high saturation magnetization and large magnetocrystalline anisotropy, as also metallic antiferromagnets. These results establish DiffCrysGen as a general platform for accelerated design of functional materials.

cond-mat.mtrl-sci↗

arXiv2025

Interfacial Control of both Magnetism and Polarization in a van der Waals Ferromagnet/Ferroelectric Heterostructure

Two-dimensional multiferroic van der Waals heterostructures provide a promising platform for the simultaneous control of distinct ferroic orders, with potential applications in magnetoelectric devices and spintronics. The practical implementation of such technologies requires 2D magnets with high Curie temperatures and strong perpendicular magnetic anisotropy (PMA). Here, based on first-principles calculations, we propose a multiferroic heterostructure composed of the room-temperature ferromagnet $\text{Fe}_3\text{Ga}\text{Te}_2$ and the ferroelectric $\text{In}_2\text{Se}_3$. We show that intercalation of Fe atoms into the van der Waals gap of the $\text{Fe}_3\text{Ga}\text{Te}_2$/$\text{In}_2\text{Se}_3$ heterostructure enhances PMA by nearly an order of magnitude relative to the pristine $\text{Fe}_3\text{Ga}\text{Te}_2$ monolayer, while simultaneously allowing electric polarization to be modulated through interfacial charge redistribution. The enhancement of PMA arises from interfacial hybridization that modifies the spin-orbit coupling of Fe $d$-orbitals. Our results demonstrate an effective pathway to engineer magnetoelectric coupling in two-dimensional multiferroic heterostructures and pave the way toward energy-efficient spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

DiffCrysGen: A Score-Based Diffusion Model for Design of Diverse Inorganic Crystalline Materials

Crystal structure generation is a foundational challenge in materials discovery, particularly in designing functional inorganic crystalline materials with desired properties. Most existing diffusion-based generative models for crystals rely on complex, hand-crafted priors and modular architectures to separately model atom types, atomic positions, and lattice parameters. These methods often require customized diffusion processes and conditional denoising, which can introduce additional model complexities and inconsistencies. Here we introduce DiffCrysGen, a fully data-driven, score-based diffusion model that jointly learns the distribution of all structural components in crystalline materials. With crystal structure representation as unified 2D matrices, DiffCrysGen bypasses the need for task-specific priors or decoupled modules, enabling end-to-end generation of atom types, fractional coordinates, and lattice parameters within a single framework. Our model learns crystallographic symmetry and chemical validity directly from large-scale datasets, allowing it to scale to complex materials discovery tasks. As a demonstration, we applied DiffCrysGen to the design of rare-earth-free magnetic materials with high saturation magnetization, showing its effectiveness in generating stable, diverse, and property-aligned candidates for sustainable magnet applications.

cond-mat.mtrl-sci↗

arXiv2024

Dimension-Dependent Critical Scaling Analysis and Emergent Competing Interaction Scales in a 2D Van der Waals magnet Cr$_{2}$Ge$_{2}$Te$_{6}$

We investigate thickness-dependent transformation from a paramagnetic to ferromagnetic phase in Cr$_{2}$Ge$_{2}$Te$_{6}$ (CGT) in bulk and few-layer flake forms. 2D Ising-like critical transition in bulk CGT occurs at $T_{c}$ = 67 K with out-of-plane magnetic anisotropy. Few-layer CGT on hBN/SiO$_{2}$/Si substrate displays the same $T_{c}$ but also exhibits a new critical transition at $T^{\prime}_c$ = 14.2 K. Here, critical scaling analysis reveals the critical exponents differ significantly from those in bulk and do not align with the known universality classes. Our Density Functional Theory (DFT) and classical calculations indicate competition between magnetocrystalline and dipolar anisotropy emerges with reduced dimensions. The observed behavior is due to minor structural distortions in low dimensional CGT, which modify the balance between spin-orbit coupling, exchange interactions and dipolar anisotropy. This triggers a critical crossover at $T^{\prime}_c$. Our study shows the emergence of a complex interplay of short- and long-range interactions below $T^{\prime}_c$ as CGT approaches the 2D limit.

cond-mat.mtrl-sci↗

arXiv2023

MagGen: A graph aided deep generative model for inverse design of stable, permanent magnets

A significant development towards inverse design of materials with well-defined target properties is reported. A deep generative model based on variational autoencoder (VAE), conditioned simultaneously by two target properties, is developed to inverse design stable magnetic materials. Structure of the physics informed, property embedded latent space of the model is analyzed using graph theory, based on the idea of similarity index. The graph idea is shown to be useful for generating new materials that are likely to satisfy target properties. An impressive ~96% of the generated materials is found to satisfy the target properties as per predictions from the target learning branches. This is a huge improvement over approaches that do not condition the VAE latent space by target properties, or do not consider connectivity of the parent materials perturbing which the new materials are generated. In such models, the fraction of materials satisfying targets can be as low as ~5%. This impressive feat is achieved using a simple real-space only representation called Invertible Real-space Crystallographic Representation (IRCR), that can be directly read from material cif files. Model predictions are finally validated by performing DFT calculations on a randomly chosen subset of materials. Performance of the present model using IRCR is comparable or superior to that of the models reported earlier. This model for magnetic material generation, MagGen, is applied to the problem of designing rare earth free permanent magnets with promising results.

cond-mat.mtrl-sci↗