5 papers · 1 filter
Charting the thermodynamic stability of hybrid perovskite alloys with machine learning
Jarno Laakso, Armi Tiihonen, Patrick Rinke
Alloy-based perovskite solar cells offer tunable properties and improved stability, but their complexity has impeded accurate modeling, hindering development. We present a machine-…
Selectivity- and Activity-Aware Catalyst Descriptors for CO Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
Prajwal Pisal, OndÅej KrejÄÃ, Patrick Rinke
Adsorption energy distributions (AEDs) have emerged as a powerful and increasingly adopted descriptor for catalytic performance in high-entropy alloys and, more recently, in conven…
Role of photonic interference in exciton-mediated magneto-optic responses
Güven Budak, Güven Budak, Christian Riedel +5
Coupled optical and magnetic excitations can give rise to remarkably strong magneto-optic responses. This is particularly evident in van der Waals magnets, such as the antiferromag…
Predicting the Thermal Behavior of Semiconductor Defects with Equivariant Neural Networks
Xiangzhou Zhu, Patrick Rinke, David A. Egger
The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationa…
Exploring Noncollinear Magnetic Energy Landscapes with Bayesian Optimization
Jakob Baumsteiger, Lorenzo Celiberti, Patrick Rinke +2
The investigation of magnetic energy landscapes and the search for ground states of magnetic materials using ab initio methods like density functional theory (DFT) is a challenging…