Research line 1
New ab initio methods
Vibrational properties are crucial to describe the structural, thermodynamic, and transport properties of materials. The standard harmonic approximation assumes that ions in solids are placed at […]
Research
We use first-principles quantum mechanical methods to understand and predict the properties of materials.
Our group is affiliated to the Department of Applied Physics and Materials Physics Center of the University of the Basque Country (UPV/EHU).
In order to overcome the limitations imposed by standard approximations, we develop new theoretical methods that give us the possibility of approaching problems in physics from a novel and advanced perspective. We apply our methods to characterize and predict hydrogen-based high-temperature superconductors, to study charge-density wave and ferroelectric phase transitions, and understand the interaction of light with lattice vibrations.
We study these research lines
Research line 2
Hydrogen-based superconductors
Since the 2015 experimental discovery of superconductivity above 200 K at high pressures, it is clear that hydrogen-based compounds can be high-temperature superconductors, at least at […]
Research line 3
Phase transitions in functional materials
Understanding the structural phase transitions of solids and their impact on physical properties is one of the most important tasks in physics. Many functional materials, like […]
Research line 4
Phonon polaritons and polarons
Controlling light down to the nanoscale is a huge challenge, especially because the wavelength of exceeds by orders of magnitude the interatomic distances. Phonon polaritons, the […]
Latest publications
Iterative learning scheme for crystal structure prediction with anharmonic lattice dynamics
Hao Gao, Yue-Wen Fang and Ion Errea
Physical Review B 113, 134113 (2026)
Search for thermodynamically stable ambient-pressure superconducting hydrides in the GNoME database
Antonio Sanna, Tiago F. T. Cerqueira, Ekin Dogus Cubuk, Ion Errea and Yue-Wen Fang
Communications Physics 9, 94 (2026)
Machine learning driven exploration of hydride superconductors at ambient pressure
Paulo R. Pires, Thalis H.B. da Silva, Kun Gao, Kaja H. Hiorth, Tiago F. T. Cerqueira, Théo Cavignac, Pierre-Paul De Breuck, Hai-Chen Wang, Đorđe Dangić, Yue-Wen Fang, Antonio Sanna, Wenwen Cui, Ion Errea, Päivi Törmä and Miguel A. L. Marques
Computational Materials Today 10, 100052 (2026)