arXiv subjects

Junaid Jami

Publications and source records attributed to Junaid Jami.

4 recordsLinked to original sources

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

Tailoring hard magnetic properties of Fe2MnSn Heusler alloy via interstitial modification: A first-principles approach

We employ first-principles calculations to explore interstitial engineering as a strategy to tailor the hard magnetic properties of Fe2MnSn Heusler alloy, establishing its potential as a rare-earth-free permanent magnet. By introducing light interstitial elements -- B, C, H, N, O, and F -- at varying concentrations (1.56-12.5 at%), we uncover significant enhancements in structural stability, magnetization, Curie temperature, and magnetocrystalline anisotropy. These dopants preferentially occupy octahedral interstitial sites in the hexagonal phase of Fe2MnSn, leading to localized lattice distortions that enhance its magnetic characteristics. Notably, at 12.5 at% doping, B, C, N, and O induce a critical transition from in-plane to out-of-plane magnetic anisotropy -- achieved without 5d or rare-earth elements -- highlighting a sustainable pathway to high-performance magnets. Among these, N-doped Fe2MnSn exhibits the highest uniaxial anisotropy (0.61 MJ/m^3), followed by the B-doped (0.44 MJ/m^3) alloy. The magnetization of the doped compounds surpasses that of conventional ferrites and gap magnets like MnAl and MnBi. The Curie temperature sees a substantial boost, reaching 1058 K for O-doped Fe2MnSn and 1000 K for the C-doped alloy. Although N-doping results in a modest increase in Tc (744 K vs. 729 K for the pristine alloy), it delivers superior hard magnetic properties, with the highest magnetic hardness (0.65) and an enhanced maximum energy product (0.36 MJ/m^3), making it a strong candidate for gap magnet applications. These findings highlight interstitial doping as a viable route to engineer rare-earth-free permanent magnets with optimized magnetic performance.

cond-mat.mtrl-sci↗

arXiv2025

Data-driven high-throughput search for the accelerated discovery of rare-earth-free permanent magnets

An integrated data-driven approach combined with a high-throughput framework based on first-principles calculations was used to discover novel rare-earth-free permanent magnets, focusing on binary alloys. Compounds were screened systematically based on their elemental composition, structure, stability, and magnetization. Density functional theory (DFT) calculations were performed on the selected candidates to evaluate their magnetocrystalline anisotropy energy (MAE) and Curie temperature (Tc), resulting in the identification of ten promising materials. A thorough literature review was done to assess reports of prior existence, which confirmed the novelty of ZnFe and Fe8N. Their ferromagnetic ground state was re-established through DFT, and structural stability was confirmed via negative formation enthalpies, phonon spectra, and elastic criteria. Tetragonal ZnFe and Fe8N exhibit high saturation magnetization (>1 T), large anisotropy constants (>0.5 MJ/m^3), and high Tc (>1200 K). Their magnetic hardness parameters (kappa = 0.85 for ZnFe and 0.70 for Fe8N) further support their potential as gap magnets. These findings highlight the efficacy of our high-throughput screening, which may serve as a theoretical blueprint for the experimental realization of these materials.

cond-mat.mtrl-sci↗

arXiv2025

Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction

Accurately predicting magnetic behavior across diverse materials systems remains a longstanding challenge due to the complex interplay of structural and electronic factors and is pivotal for the accelerated discovery and design of next-generation magnetic materials. In this work, a refined descriptor is proposed that significantly improves the prediction of two critical magnetic properties -- magnetic ordering (Ferromagnetic vs. Ferrimagnetic) and magnetic moment per atom -- using only the structural information of materials. Unlike previous models limited to Mn-based or lanthanide-transition metal compounds, the present approach generalizes across a diverse dataset of 5741 stable, binary and ternary, ferromagnetic and ferrimagnetic compounds sourced from the Materials Project. Leveraging an enriched elemental vector representation and advanced feature engineering, including nonlinear terms and reduced matrix sparsity, the LightGBM-based model achieves an accuracy of 82.4% for magnetic ordering classification and balanced recall across FM and FiM classes, addressing a key limitation in prior studies. The model predicts magnetic moment per atom with a correlation coefficient of 0.93, surpassing the Hund's matrix and orbital field matrix descriptors. Additionally, it accurately estimates formation energy per atom, enabling assessment of both magnetic behavior and material stability. This generalized and computationally efficient framework offers a robust tool for high-throughput screening of magnetic materials with tailored properties.

cond-mat.mtrl-sci↗