collaborators

5 papers

cond-mat.mtrl-sci2026

Predicting Crystal Structures and Ionic Conductivities in LiYClBr Halide Solid Electrolytes Using a Fine-Tuned Machine Learning Interatomic Potential

Jonas Böhm, Aurélie Champagne

Understanding ionic transport in halide solid electrolytes is essential for advancing next-generation solid-state batteries. This work demonstrates the effectiveness of fine-tuning…

cond-mat.mtrl-sci2025

Tunable electronic energy level alignment and exciton diversity in organic-inorganic van der Waals heterostructures

Aurélie Champagne, Olugbenga Adeniran, Jonah B. Haber +3

van der Waals stacking of two-dimensional (2D) materials offers a powerful platform for engineering material interfaces with tailored electronic and optical properties. While most…

cond-mat.mtrl-sci2025

The reliability of hybrid functionals for accurate fundamental and optical gap prediction of bulk solids and surfaces

Francisca Sagredo, María Camarasa-Gómez, Francesco Ricci +3

Hybrid functionals have been considered insufficiently reliable for the prediction of band gaps in solids and surfaces. We revisit this issue with a new generation of optimally-tun…

cond-mat.mtrl-sci2025

Bright hybrid excitons in molecularly tunable bilayer crystals

Tomojit Chowdhury, Aurélie Champagne, Patrick Knüppel +8

Bilayer crystals, built by stacking crystalline monolayers, generate interlayer potentials that govern excitonic phenomena but are constrained by fixed covalent lattices and orient…

cond-mat.mtrl-sci2025

Molecular tuning of excitons in four-atom-thick hybrid bilayer crystals

Tomojit Chowdhury, Aurélie Champagne, Patrick Knüppel +6

Bilayer crystals, formed by stacking monolayers of two-dimensional (2D) crystals, create interlayer potentials that govern excitonic phenomena but are constrained by their fixed co…