activity
20232026
most citedMachine learning for ultra high throughput screening of organic solar cells: Solving the needle in the hay stack problem

27 citations · 58 across the 9 of their papers we have counts for

collaborators

9 papers

cond-mat.mtrl-sci2026

Mobility-lifetime relation links photodegradation in spin-coated and gravure-printed organic solar cells

Maria Saladina, Paul Weitz, David Holzner +24

The degradation mechanisms of organic solar cells (OSCs) have been studied primarily in spin-coated, laboratory-scale devices, whereas scalable processing modifies the device archi…

cond-mat.mtrl-sci2026★ 1 cited

Rethinking Charge Transport and Recombination in Donor-diluted Organic Solar Cells

Chen Wang, Christopher Wöpke, Toni Seiler +12

We systematically investigate PM6:Y12 bulk-heterojunction solar cells with donor fractions ranging from 1% to 45%, linking morphology, charge transport, and recombination to device…

cond-mat.mtrl-sci2026

Bridging the lab-to-fab gap in non-fullerene organic solar cells via gravure printing

Svitlana Taranenko, Chen Wang, David Holzner +10

Organic solar cells have reached record efficiencies with non-fullerene acceptors, yet their translation to industrial printing remains a critical bottleneck. Here we report the hi…

math.NA2026

Modeling and Simulation of Device Performance in Organic Photovoltaics

Pelin Çiloğlu, Carmen Tretmans, Carsten Deibel +4

We present a pipeline to study the device performance of organic solar cells in silico. We introduce a mathematical model that includes the dynamics of excitons as well as their di…

physics.app-ph2025

Visualizing the Link Between Nanomorphology and Energetic Disorder in 3D Organic Solar Cells

Pelin Çiloğlu, Carmen Tretmans, Carsten Deibel +3

The performance of organic bulk heterojunction (BHJ) solar cells is highly sensitive to both nanomorphology and energetic disorder arising from microscopic molecular packing and st…

physics.app-ph2025

A Framework to Pinpoint Bottlenecks in Emerging Solar Cells and Disordered Devices via Differential Machine Learning

Cai Williams, Chen Wang, Alexander Ehm +4

A key challenge in the development of materials for the next generation of solar cells, sensors and transistors is linking macroscopic device performance to underlying microscopic…

cond-mat.mtrl-sci2024★ 27 cited

Transport resistance dominates the fill factor losses in record organic solar cells

Chen Wang, Roderick C. I. MacKenzie, Uli Würfel +4

Organic photovoltaics are a promising solar cell technology well-suited to mass production using roll-to-roll processes. The efficiency of lab-scale solar cells has exceeded 20% an…

physics.app-ph2023★ 3 cited

The Physical Meaning of Time-Delayed Collection Field Transients from Disordered Devices

Markus Hußner, Carsten Deibel, Roderick Charles Ian MacKenzie

Charge carrier mobility and recombination determine the performance of many opto-electronic devices such as solar cells, sensors and light-emitting diodes. Understanding how these…

physics.app-ph2023★ 27 cited

Machine learning for ultra high throughput screening of organic solar cells: Solving the needle in the hay stack problem

Markus Hußner, Pacalaj A. Richard, Olaf G. Müller-Dieckert +12

Over the last two decades the organic solar cell community has synthesised tens of thousands of novel polymers and small molecules in the search for an optimum light harvesting mat…