8 papers
Charting the thermodynamic stability of hybrid perovskite alloys with machine learning
Jarno Laakso, Armi Tiihonen, Patrick Rinke
Alloy-based perovskite semiconductor materials offer tunable properties and improved stability, but their complexity has impeded accurate modeling, hindering development of solar c…
Benchmarking machine-learned interatomic potentials for molecular infrared spectroscopy
Nitik Bhatia, Ondrej Krejci, Patrick Rinke
Machine learning has transformed the field of atomistic simulations by enabling the development of interatomic potentials that are computationally efficient and highly accurate. Th…
Selectivity- and Activity-Aware Catalyst Descriptors for CO Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
Prajwal Pisal, Ondřej Krejčí, 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…
Bayesian Optimization for Mixed-Variable Problems in the Natural Sciences
Yuhao Zhang, Ti John, Matthias Stosiek +1
Optimizing expensive black-box objectives over mixed search spaces is a common challenge across the natural sciences. Bayesian optimization (BO) offers sample-efficient strategies…
Role of photonic interference in exciton-mediated magneto-optic responses
Güven Budak, Christian Riedel, Akashdeep Kamra +4
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…
Active Learning of Molecular Data for Task-Specific Objectives
Kunal Ghosh, Milica Todorović, Aki Vehtari +1
Active learning (AL) has shown promise for being a particularly data-efficient machine learning approach. Yet, its performance depends on the application and it is not clear when A…