From the 1 of 12 linked papers with an AI index.
12 papers
Amorphous materials as a frontier challenge for universal interatomic potentials
Natascia L. Fragapane, Volker L. Deringer
The paper evaluates how well current pre‑trained machine‑learned interatomic potentials work for amorphous (non‑crystalline) materials, introduces a benchmark dataset for such syst…
Atomistic Mechanisms of Hard Carbon Formation from Polyvinylidene Chloride
Litong Wu, Zitong Wu, Zakariya El-Machachi +1
Hard carbons are a class of disordered materials with widespread application in energy storage. Despite decades of research, their atomistic formation mechanisms have remained elus…
A Defect-Free Model of Amorphous Silicon with Pristine Electronic Structure
Louise A. M. Rosset, Chinonso Ugwumadu, Stephen R. Elliott +2
Amorphous silicon (a-Si) is understood to be the canonical continuous random network material, ideally defined by fully fourfold coordination. Here, we show that a defect-free ('id…
Synthetic pre-training of graph-network models for predicting solid-state NMR parameters
Chiheb Ben Mahmoud, Carlos Bornes, Christopher J. Heard +3
Nuclear magnetic resonance (NMR) is a powerful probe of atomic structure, but accurate quantum-mechanical predictions of tensorial NMR parameters are computationally demanding. Thi…
An Accurate Tensorial Model for Prediction of Full Zeolite NMR Spectra
Carlos Bornes, Chiheb Ben Mahmoud, Volker L. Deringer +2
Solid state nuclear magnetic resonance (ss-NMR) is one of the most sensitive and popular techniques for structure elucidation in geometrically complex crystalline materials, such a…
Regularity Priors for the Linear Atomic Cluster Expansion
James P. Darby, Joe D. Morrow, Albert P. Bartók +3
Machine-learned interatomic potentials enable large systems to be simulated for long time scales at near ab-initio accuracy. This accuracy is achieved by fitting extremely flexible…