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Dynamical Disorder in the Mesophase Ferroelectric HdabcoClO4: A Machine-Learned Force Field Study
Elin Dypvik Sødahl, Jesús Carrete, Georg K. H. Madsen +1
Hybrid molecular ferroelectrics with orientationally disordered mesophases offer significant promise as lead-free alternatives to traditional inorganic ferroelectrics owing to prop…
Machine-learning potential for phonon transport in AlN with defects in multiple charge states
Ying Dou, Koji Shimizu, Jesús Carrete +2
Understanding phonon transport properties in defect-laden AlN is important for their device applications. Here, we construct a machine-learning potential to describe phonon transpo…
First- and second-order quantum phase transitions in the long-range unfrustrated antiferromagnetic Ising chain
Víctor Herráiz-López, Sebastián Roca-Jerat, Manuel Gallego +4
We study the ground-state phase diagram of an unfrustrated antiferromagnetic Ising chain with longitudinal and transverse fields in the full range of interactions: from all-to-all…
Neural-network-enabled molecular dynamics study of HfO phase transitions
Sebastian Bichelmaier, Jesús Carrete, Georg K. H. Madsen
The advances of machine-learned force fields have opened up molecular dynamics (MD) simulations for compounds for which ab-initio MD is too resource-intensive and phenomena for whi…
Transformer Wave Function for Quantum Long-Range models
Sebastián Roca-Jerat, Manuel Gallego, Fernando Luis +2
We employ a neural-network architecture based on the Vision Transformer (ViT) architecture to find the ground states of quantum long-range models, specifically the transverse-field…
A neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia
Sebastian Bichelmaier, Jesús Carrete, Ralf Wanzenböck +2
Phonon-based approaches and molecular dynamics are widely established methods for gaining access to a temperature-dependent description of material properties. However, when a comp…