3 papers
cond-mat.mtrl-sci2026
Amorphous materials as a frontier challenge for universal interatomic potentials
Natascia L. Fragapane, Volker L. Deringer
Pre-trained or 'foundational' machine-learned interatomic potentials (MLIPs) are now widely used in materials modelling. However, early pre-trained models and benchmarks have large…
cond-mat.mtrl-sci2025
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…
physics.comp-ph2024
An automated framework for exploring and learning potential-energy surfaces
Yuanbin Liu, Joe D. Morrow, Christina Ertural +6
Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing mach…