8 citations · 9 across the 7 of their papers we have counts for
5 papers · 1 filter
Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials
Natascia L. Fragapane, Volker L. Deringer
With the growing availability of machine-learned interatomic potential (MLIP) models for materials simulations, there is an increasing demand for robust, automated, and chemically…
Atomic cluster expansion potential for the Si-H system
Louise A. M. Rosset, Volker L. Deringer
The silicon-hydrogen system is of key interest for solar-cell devices, including both crystalline and amorphous modifications. Elemental amorphous Si is now well understood, but th…
Autonomous interpretation of atomistic scattering data
Andy S. Anker, John L. A. Gardner, Louise A. M. Rosset +2
Materials with bespoke properties have long been identified by computational searches, and their experimental realisation is now coming within reach through autonomous laboratories…
Machine-learning-driven modelling of amorphous and polycrystalline BaZrS
Laura-Bianca Paşca, Yuanbin Liu, Andy S. Anker +2
The chalcogenide perovskite material BaZrS is of growing interest for emerging thin-film photovoltaics. Here we show how machine-learning-driven modelling can be used to desc…
The Zintl-Klemm Concept in the Amorphous State: A Case Study of Na-P Battery Anodes
Litong Wu, Volker L. Deringer
The Zintl-Klemm concept has long been used to explain and predict the bonding, and thereby the structures, of crystalline solid-state materials. We apply this concept to the amorph…