1.6k citations · 6.5k across the 45 of their papers we have counts for
45 papers
Energy Efficiency Maximization for Integrated Sensing and Communication in IoT C-RAN
Le Tung Giang, Nguyen Xuan Tung, Trinh Van Chien +2
Integrated sensing and communication (ISAC) with cloud radio access networks (C-RAN) unifies two foundational technologies for the Internet of Things (IoT), enabling both high-data…
A Linear-Scaling, Charge-Aware Foundation Potential for Atomistic Simulations
Tsz Wai Ko, Runze Liu, Adesh Rohan Mishra +3
Electrostatics govern charge transfer and reactivity in materials. However, most foundation potentials (FPs) either neglect explicit electrostatic interactions or come at prohibiti…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
Tsz Wai Ko, Bowen Deng, Marcel Nassar +7
Graph deep learning models, which incorporate a natural inductive bias for a collection of atoms, are of immense interest in materials science and chemistry. Here, we introduce the…
Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials
Tsz Wai Ko, Shyue Ping Ong
Machine learning potentials (MLPs) have become an indispensable tool in large-scale atomistic simulations because of their ability to reproduce ab initio potential energy surfaces…