6 papers
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…
Atomic-scale tunable phonon transport at tailored grain boundaries and Their Impact on Thermal Conductivity
Xiaowang Wang, Chaitanya A. Gadre, Wanjuan Zou +16
Grain boundaries (GBs) strongly influence thermal transport in crystalline solids by disrupting lattice periodicity and scattering phonons. Due to the atomic-level disorder and str…
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…
Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization
Yiming Chen, Chi Chen, Inhui Hwang +9
X-ray absorption spectroscopy (XAS) is a commonly-employed technique for characterizing functional materials. In particular, x-ray absorption near edge spectra (XANES) encodes loca…
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…