arXiv subjects

Osman Goni Ridwan

Publications and source records attributed to Osman Goni Ridwan.

6 recordsLinked to original sources

arXiv2026

Benchmarking Universal Machine Learning Force Fields for Crystal Structure Prediction of High-Energy Molecular Systems

Recent developments of universal machine learning interatomic potentials (UMLIPs) offer a fast route for screening molecular crystals based on geometry relaxation and energy ranking, but their reliability across chemically diverse energetic materials remains elusive. In particular, it is unclear whether or not these UMLIPs are over-sensitive to break the desired molecular connectivity for relaxing the periodic crystals. Herein we tested the hypothesis that classical force-field pre-relaxation can provide a more suitable starting geometry for subsequent UMLIP relaxation on a large database of high energy molecular crystals. Three models (MACE, MACE-OFF and UMA) in conjunction with the General Amber Force Field (GAFF) were applied to test this hypothesis. Among them, direct MACE-OFF and UMA showed very high relaxation success and preserved the reference geometries most closely, but they still exhibit failures for some rare cases. Using GAFF pre-relaxation can systematically reduce the number of failed relaxations with lower computational costs. Our comparative failure and robustness analyses revealed distinct trade-offs among the evaluated models. Among them, MACE-OFF achieves a better compromise between potential energy surface smoothness, structural fidelity, and stress convergence, serving as a good choice to provide a reliable foundation for automated structural optimization.

cond-mat.mtrl-sci↗

arXiv2026

High-throughput Discovery of Magnetic Rare Earth Transition Metal Alloys

We present an accelerated materials discovery framework that combines diffusion-based crystal structure generation with hierarchical screening to identify new rare-earth--transition-metal magnets simultaneously achieving high magnetization and thermodynamic stability. Using this workflow, we systematically explored over 3000 binary (R-T) and ternary (R-T-T$'$) compositions spanning R~$\in \{\text{Y, Sm}\}$, T~$\in \{\text{Fe, Co, Ni}\}$, and T$' \in \{\text{Ti, V, Cr, Mn, Cu, Zn}\}$, and filtered approximately 240{,}000 generated crystal structures through machine-learning interatomic potential prescreening and spin-polarized density functional theory validation. We identify 300+ low-energy magnetic candidates within 0.1~eV/atom above the convex hull at the DFT level, including 5 thermodynamically stable phases. The highest saturation magnetization reaches ${\sim}1.8$~T in Fe-rich binary and ternary phases (SmFe$_{12}$, YFe$_{12}$, YFe$_{18}$Ti and Sm$_2$Fe$_{16}$Mn). Symmetry analysis reveals that the majority of ternary candidates are subgroup derivatives of known binary prototypes through Wyckoff site splitting that accommodates T$'$ substitution. Site-resolved magnetic moment analysis further shows that Mn aligns ferromagnetically with the Fe sublattice with minimal magnetization loss, whereas Cr couples antiferromagnetically, providing systematic guidance for dopant selection. These findings demonstrate a generalizable strategy for targeted magnetic materials discovery and suggest that extending generative searches to larger unit cells ($>$20 atoms) with higher Fe fractions is a promising route toward stable phases with saturation magnetization exceeding 1.8~T.

cond-mat.mtrl-sci↗

arXiv2026

Ab-initio Crystal Structure Determination from Powder X-Ray Diffraction

Determining crystal structures from powder X-ray diffraction (PXRD) has been a significant challenge in materials science, particularly when experimental data contain noise or the target structure has a high complexity. While recent AI generative models show promise for rapid structure generation, they predominantly employ data-driven approaches to learn direct mappings between PXRD patterns and crystal structures, often failing on complex or out-of-distribution cases. In this work, we present a hybrid ab-initio approach that decomposes structure determination into a two-stage optimization problem: (1) discrete selection of space group symmetry, unit cell parameters, and Wyckoff site combinations; and (2) continuous optimization of atomic coordinates within the selected Wyckoff positions. By integrating AI-based techniques for peak profile analysis, density estimation and energy minimization with physics-informed constraints, our method systematically overcomes limitations of purely data-driven PXRD solvers. We demonstrate that this hierarchical optimization framework enables robust structure determination even for challenging cases with high structural complexity or limited experimental data quality. Our approach provides a principled pathway for incorporating crystallographic knowledge into AI models for more reliable and generalizable crystal structure determination.

cond-mat.mtrl-sci↗

arXiv2026

Crystal Representation in the Reciprocal Space

In crystallography, a structure is typically represented by the arrangement of atoms in the direct space. Furthermore, space group symmetry and Wyckoff site notations are applied to characterize crystal structures with only a few variables. While this representation is effective for data records and human learning, it lacks one-to-one correspondence between the crystal structure and its representation. This is problematic for many applications, such as crystal structure determination, comparison, and more recently, generative model learning. To address this issue, we propose to represent crystals in a four-dimensional (4D) reciprocal space featured by their Cartesian coordinates and scattering factors, which can naturally handle translation invariance and space group symmetry with the help of structure factors. In order to achieve rotational invariance, the 4D coordinates are then transformed into a power spectrum representation under the orthogonal spherical harmonic and radial basis. Hence, this representation captures both periodicity and symmetry of the crystal structure while also providing a continuous representation of the atomic positions and cell parameters in the direct space. Its effectiveness is demonstrated by applying it to several crystal structure matching and reconstruction tasks.

cond-mat.mtrl-sci↗

arXiv2026

Crystal Generation using the Fully Differentiable Pipeline and Latent Space Optimization

We present a materials generation framework that couples a symmetry-conditioned variational autoencoder (CVAE) with a differentiable SO(3) power spectrum objective to steer candidates toward a specified local environment under the crystallographic constraints. In particular, we implement a fully differentiable pipeline to enable batch-wise optimization on both direct and latent crystallographic representations. Using the GPU acceleration, this implementation achieves about fivefold speed compared to our previous CPU workflow, while yielding comparable outcomes. In addition, we introduce the optimization strategy that alternatively performs optimization on the direct and latent crystal representations. This dual-level relaxation approach can effectively escape local minima defined by different objective gradients, thus increasing the success rate of generating complex structures satisfying the target local environments. This framework can be extended to systems consisting of multi-components and multi-environments, providing a scalable route to generate material structures with the target local environment.

cond-mat.mtrl-sci↗

arXiv2025

AI-Assisted Rapid Crystal Structure Generation Towards a Target Local Environment

In the field of material design, traditional crystal structure prediction approaches require extensive structural sampling through computationally expensive energy minimization methods using either force fields or quantum mechanical simulations. While emerging artificial intelligence (AI) generative models have shown great promise in generating realistic crystal structures more rapidly, most existing models fail to account for the unique symmetries and periodicity of crystalline materials, and they are limited to handling structures with only a few tens of atoms per unit cell. Here, we present a symmetry-informed AI generative approach called Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal) that overcomes these limitations. Our method generates initial structures using AI models trained on an augmented small dataset, and then optimizes them using machine learning structure descriptors rather than traditional energy-based optimization. We demonstrate the effectiveness of LEGO-xtal by expanding from 25 known low-energy sp2 carbon allotropes to over 1,700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and next-generation battery materials.

cond-mat.mtrl-sci↗