From the 1 of 10 linked papers with an AI index.
10 papers
Fast and Accurate Foundation Models for Equivariant Machine-Learned Interatomic Potentials
Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang +12
The paper presents fast and accurate equivariant machine‑learned interatomic potentials (NequIP and Allegro) as foundation models that scale to ultra‑large datasets while maintaini…
Multiscale light-matter dynamics in quantum materials: from electrons to topological superlattices
Taufeq Mohammed Razakh, Thomas Linker, Ye Luo +12
Light-matter dynamics in topological quantum materials enables ultralow-power, ultrafast devices. A challenge is simulating multiple field and particle equations for light, electro…
Coupled reaction and diffusion governing interface evolution in solid-state batteries
Jingxuan Ding, Laura Zichi, Matteo Carli +4
Understanding and controlling the atomistic-level reactions governing the formation of the solid-electrolyte interphase (SEI) is crucial for the viability of next-generation solid…
High-performance training and inference for deep equivariant interatomic potentials
Chuin Wei Tan, Marc L. Descoteaux, Mit Kotak +11
Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in a…
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
Menghang Wang, Jingxuan Ding, Grace Xiong +8
In solid acid solid electrolytes CsHPO and CsHSO, mechanisms of fast proton conduction have long been debated and attributed to either local proton hopping or polyanion…
Atomistic evolution of active sites in multi-component heterogeneous catalysts
Cameron J. Owen, Lorenzo Russotto, Christopher R. O'Connor +4
Multi-component metal nanoparticles (NPs) are of paramount importance in the chemical industry, as most processes therein employ heterogeneous catalysts. While these multi-componen…