most citedDis-GEN: Disordered crystal structure generation

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cond-mat.mtrl-sci2026

A sulfonitride transparent conductive thin film with ultra-high refractive index

Eugène Bertin, Shima Kadkhodazadeh, José María Castillo-Robles +9

With the rise of AI-assisted materials screening, extraordinary properties are now frequently predicted in experimentally uncharted material systems, highlighting the need to devel…

cond-mat.mtrl-sci2026

Designing dislocation-driven polar vortex networks in twisted perovskites

William Sandholt, Nicolas Gauquelin, John Mangeri +21

Twisting two atomic layers produces a geometric moire pattern, but bonding-induced interfacial reconstruction fundamentally transforms this into an ordered dislocation network - a…

cond-mat.mtrl-sci2026★ 1 cited

Importance of Electronic Entropy for Machine Learning Interatomic Potentials

Martin Hoffmann Petersen, Steen Lysgaard, Arghya Bhowmik +2

Machine learning interatomic potentials (MLIPs) enable large-scale atomistic simulations but remain challenged in describing mixed-valence materials where charge ordering strongly…

cond-mat.mtrl-sci2025★ 2 cited

Dis-GEN: Disordered crystal structure generation

Martin Hoffmann Petersen, Ruiming Zhu, Haiwen Dai +6

A wide range of synthesized crystalline inorganic materials exhibit compositional disorder, where multiple atomic species partially occupy the same crystallographic site. As a resu…

cs.LG2025

Kinetic Langevin Diffusion for Crystalline Materials Generation

François Cornet, Federico Bergamin, Arghya Bhowmik +3

Generative modeling of crystalline materials using diffusion models presents a series of challenges: the data distribution is characterized by inherent symmetries and involves mult…