Results 81 to 90 of about 5,728,670 (281)

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
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

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
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, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

River‐Floodplain Metacommunities as Complex Networks: The Interplay of Species Interactions, Dispersal, and Environment

Environmental DNA
Dynamic heterogeneous metacommunities can be analyzed as complex networks. However, the interplay of species interactions, local environmental conditions, and spatiotemporal dispersal remains poorly understood.
Andrea Funk, István Czeglédi, Tibor Erős, Lukas Landler, Paul Meulenbroek, Didier Pont, Sonia Recinos Brizuela, Alice Valentini, Thomas Hein   +8 more
doaj   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
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, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
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

Multistream Dynamic Bayesian Network for Meeting Segmentation [PDF]

, 2005
This paper investigates the automatic analysis and segmentation of meetings. A meeting is analysed in terms of individual behaviours and group interactions, in order to decompose each meeting in a sequence of relevant phases, named meeting actions. Three feature families are extracted from multimodal recordings: prosody from individual lapel microphone
Alfred Dielmann, Steve Renals
openaire   +3 more sources

Artificial Intelligence Meets Micro/Nanorobotics

Advanced Materials, EarlyView.
Artificial intelligence is transforming micro‐ and nanorobots from externally controlled, task‐specific machines into adaptive, autonomous systems. Machine learning, multimodal perception, digital twins, AI‐guided materials and geometry design enhance propulsion, localization, decision‐making, whichaccelerates clinical and environmental applications ...
Fatma M. Yurtsever, Minitha Cherukutty Ramakrishnan, Ian Kuula Ross, Ivan Zelinka, Hamed Shahsavan, Michal Otyepka, Martin Pumera   +6 more
wiley   +1 more source

Indirect Causes in Dynamic Bayesian Networks Revisited

Journal of Artificial Intelligence Research, 2017
Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time.
Alexander Motzek, Ralf Möller 0001
openaire   +4 more sources

Electrolyte Engineering Challenges and Opportunities for Next‐Generation Aqueous Ammonium‐Ion Batteries

Advanced Materials, EarlyView.
Aqueous ammonium‐ion batteries (AAIBs) face a hydrogen‐bond paradox: the HB network enables fast NH4+ transport but triggers water decomposition. This review dissects this dilemma, evaluates multiple electrolyte engineering strategies, and outlines four future directions for next‐generation AAIB design ABSTRACT Aqueous ammonium‐ion batteries (AAIBs ...
Zi‐Hang Huang, Xiaoyu Liu, Shuai Mao, Lingfeng Zhu, Zhijun Wu, Hongge Pan, Tianyi Ma   +6 more
wiley   +1 more source

Sensorimotor coupling via Dynamic Bayesian Networks

2008 IEEE International Conference on Robotics and Automation, 2008
In this paper we consider the problem of sensorimotor coordination in a Bayesian framework. To this end we introduce a novel kind of Dynamic Bayesian Network serving as the core tool to integrate active vision and task-constrained motor behaviors. The proposed system is put into work by addressing the challenging task of realistic drawing performed by ...
Ruben Coen Cagli, Paolo Napoletano, Paolo Coraggio, Giuseppe Boccignone, Agostino De Santis   +4 more
openaire   +4 more sources