Results 121 to 130 of about 4,727,432 (231)
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley +1 more source
Correlated Charge Transport in an Organic Coulomb Glass
ABSTRACT Advances in the development of organic field‐effect transistors (OFETs), electrically gated organic semiconductors (EGOFETs), and organic electrochemical transistors (OECTs) allow for the operation of these devices at very high charge‐carrier densities, where Coulomb interactions between carriers can be expected to become significant.
Magdalena Sophie Dörfler +3 more
wiley +1 more source
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
wiley +1 more source
Tone Mapping of HDR Images via Meta-Guided Bayesian Optimization and Virtual Diffraction Modeling
This paper proposes a novel image tone-mapping framework that incorporates meta-learning, a psychophysical model, Bayesian optimization, and light-field virtual diffraction.
Deju Huang +8 more
doaj +1 more source
Anytime Probabilistically Constrained Provably Convergent Online Belief Space Planning
Taking into account future risk is essential for an autonomously operating robot to find online not only the best but also a safe action to execute. In this paper, we build upon the recently introduced formulation of probabilistic belief-dependent constraints.
Andrey Zhitnikov, Vadim Indelman
openaire +4 more sources
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Rethinking Charge Transport and Recombination in Donor‐Diluted Organic Solar Cells
Organic solar cells with 1–45% PM6 content in Y12 were studied to link structure and charge dynamics to performance. The conductivity follows a 3D percolation model without a sharp threshold. Donor dilution preserves the photogeneration yield, but limits the fill factor due to transport resistance losses.
Chen Wang +14 more
wiley +1 more source
PurposeWe propose an accelerated Bayesian optimization framework for tuning the learning rate of CNN+LSTM models in soil analysis, addressing the computational inefficiency of traditional Gaussian Process (GP)-based methods.
Xiaolong Chen +3 more
doaj +1 more source
We show that sol‐gel‐fractured indium–magnesium oxide combines deep‐ultraviolet responsivity, high carrier mobility, and an excellent memory dynamic range. This unique materials platform enables deep‐ultraviolet long‐afterglow light‐emitting devices with multifunctional integration.
Zhongshi Ju +9 more
wiley +1 more source
Data‐Driven Materials Science for Energy‐Sustainable Applications
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley +1 more source

