22 citations · 35 across the 10 of their papers we have counts for
11 papers
How Thermodynamically Accessible are Quaternary Mixed-Metal Chalcohalides?
Pascal Henkel, G. Krishnamurthy Grandhi, Jarno Laakso +6
Quaternary mixed-metal chalcohalides (MMCHs) are a promising mixed-anion semiconductor family, but their thermodynamic accessibility remains largely unexplored. We combine density…
An interpretable molecular descriptor for machine learning predictions in atmospheric science
Linus Lind, Hilda Sandström, Patrick Rinke
The study of aerosol formation and chemistry using machine learning is limited by the lack of molecular descriptors suited to atmospheric compounds. Interpretable models are partic…
MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy
Nitik Bhatia, Ondrej Krejci, Silvana Botti +2
Machine-learned interatomic potentials (MLIPs) have shown significant promise in predicting infrared spectra with high fidelity. However, the absence of general-purpose MLIPs that…
Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
Nitik Bhatia, Patrick Rinke, Ondrej Krejci
Infrared (IR) spectroscopy is a pivotal analytical tool as it provides real-time molecular insight into material structures and enables the observation of reaction intermediates in…
Efficient dataset generation for machine learning perovskite alloys
Henrietta Homm, Jarno Laakso, Patrick Rinke
Lead-based perovskite solar cells have reached high efficiencies, but toxicity and lack of stability hinder their wide-scale adoption. These issues have been partially addressed th…
Design Rules for Optimizing Quaternary Mixed-Metal Chalcohalides
Pascal Henkel, Jingrui Li, Patrick Rinke
Quaternary mixed-metal M(II)2M(III)Ch2X3 chalcohalides are an emerging material class for photovoltaic absorbers that combines the beneficial optoelectronic properties of lead-base…
Machine Learning Accelerated Descriptor Design for Catalyst Discovery in CO to Methanol Conversion
Prajwal Pisal, Ondrej Krejci, Patrick Rinke
Transforming CO into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining hig…
Precision benchmarks for solids: G0W0 calculations with different basis sets
Maryam Azizi, Francisco A. Delesma, Matteo Giantomassi +10
The GW approximation within many-body perturbation theory is the state of the art for computing quasiparticle energies in solids. Typically, Kohn-Sham (KS) eigenvalues and eigenfun…
Similarity-Based Analysis of Atmospheric Organic Compounds for Machine Learning Applications
Hilda Sandström, Patrick Rinke
The formation of aerosol particles in the atmosphere impacts air quality and climate change, but many of the organic molecules involved remain unknown. Machine learning could aid i…
Validation of the GreenX library time-frequency component for efficient GW and RPA calculations
Maryam Azizi, Jan Wilhelm, Dorothea Golze +5
Electronic structure calculations based on many-body perturbation theory (e.g. GW or the random-phase approximation (RPA)) require function evaluations in the complex time and freq…
Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework
Francisco A. Delesma, Moritz Leucke, Dorothea Golze +1
Four-center two-electron Coulomb integrals routinely appear in electronic structure algorithms. The resolution-of-the-identity (RI) is a popular technique to reduce the computation…