arXiv subjects

Mohammed Al-Fahdi

Publications and source records attributed to Mohammed Al-Fahdi.

4 recordsLinked to original sources

arXiv2025

Accelerating Discovery of Extreme Lattice Thermal Conductivity by Crystal Attention Graph Neural Network (CATGNN) Using Chemical Bonding Intuitive Descriptors

Designing materials with targeted lattice thermal conductivity (LTC) demands electronic-level insight into chemical bonding. We introduce two bonding descriptors, namely normalized negative integrated crystal orbital Hamilton populations (-ICOHP) and normalized integrated crystal orbital bond index (ICOBI), that strongly correlate with LTC and rattling (mean-squared displacement), surpassing empirical rules and the unnormalized -ICOHP across >4,500 inorganic crystals by first-principles. We train a Crystal Attention Graph Neural Network (CATGNN) to predict these descriptors and screen ~200,000 database structures for extreme LTCs. From 367 (533) candidates with low (high) normalized -ICOHP and normalized ICOBI, first-principles validation identifies 106 dynamically stable compounds with LTC <5 W/mK (68% <2 W/mK) and 13 stable compounds with LTC >100 W/mK. The descriptors' low cost and clear physical meaning provide a rapid, reliable route to high-throughput discovery and inverse design of crystalline materials with ultralow or ultrahigh LTC for applications in thermal insulation, thermoelectrics, and electronics cooling.

cond-mat.mtrl-sci↗

arXiv2025

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. While their capability for modeling phonon properties is emerging, systematic benchmarking across chemically diverse systems remains limited. We evaluate six recent uMLPs (EquiformerV2, MatterSim, MACE, and CHGNet) on 2,429 crystalline materials from the Open Quantum Materials Database. Models were used to compute atomic forces in displaced supercells, derive interatomic force constants (IFCs), and predict phonon properties including lattice thermal conductivity (LTC), compared with density functional theory (DFT) and experimental data. The EquiformerV2 pretrained model trained on the OMat24 dataset exhibits strong performance in predicting atomic forces and third-order IFC, while its fine-tuned counterpart consistently outperforms other models in predicting second-order IFC, LTC, and other phonon properties. Although MACE and CHGNet demonstrated comparable force prediction accuracy to EquiformerV2, notable discrepancies in IFC fitting led to poor LTC predictions. Conversely, MatterSim, despite lower force accuracy, achieved intermediate IFC predictions, suggesting error cancellation and complex relationships between force accuracy and phonon predictions. This benchmark guides the evaluation and selection of uMLPs for high-throughput screening of materials with targeted thermal transport properties.

cond-mat.mtrl-sci↗

arXiv2022

Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints

Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts' heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700\% compared to FTCP, one of the latest structure generators and by more than 45\% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1,869 materials out of 2,000 are successfully optimized and deposited into the Carolina Materials Database \url{www.carolinamatdb.org}, of which 39.6\% have negative formation energy and 5.3\% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability.

cond-mat.mtrl-sci↗

arXiv2021

High-throughput discovery of novel cubic crystal materials using deep generative neural networks

High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of quantity and diversity of known materials deposited in the current materials repositories such as ICSD and OQMD. Recent progress in machine learning and especially deep learning have enabled a generative strategy that learns implicit chemical rules for creating chemically valid hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generation of novel cubic crystal structures. When trained on 375,749 ternary crystal materials from the OQMD database, we show that our model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such new materials (all of them are either ternary or quarternary) have been verified by DFT based phonon dispersion stability check, several of which have been found to potentially have exceptional functional properties. Considering the importance of cubic materials in wide applications such as solar cells and lithium batteries, our GAN model provides a promising approach to significantly expand the current repository of materials, enabling the discovery of new functional materials via screening. The new crystal structures finally verified by DFT are freely accessible at our Carolina Materials Database http://www.carolinamatdb.org.

cond-mat.mtrl-sci↗