146 citations · 164 across the 4 of their papers we have counts for
5 papers · 1 filter
In the age of big data and AI: do we still need chemical bonds?
Mohammed Al-Fahdi, Aakash Naik, Katharina Ueltzen +4
Chemical bonding is the language through which chemistry rationalizes materials structure, stability, properties, and reactivity. Meanwhile, modern deep-learning models achieve rem…
A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al-Fahdi +3
The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. Whil…
Accelerating Discovery of Extreme Lattice Thermal Conductivity by Crystal Attention Graph Neural Network (CATGNN) Using Chemical Bonding Intuitive Descriptors
Mohammed Al-Fahdi, Riccardo Rurali, Jianjun Hu +2
Designing materials with targeted lattice thermal conductivity (LTC) demands electronic-level insight into chemical bonding. We introduce two bonding descriptors, namely normalized…
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints
Yong Zhao, Edirisuriya M. Dilanga Siriwardane, Zhenyao Wu +4
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 labo…
High-throughput discovery of novel cubic crystal materials using deep generative neural networks
Yong Zhao, Mohammed Al-Fahdi, Ming Hu +4
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 quan…