Results 91 to 100 of about 9,437,008 (290)

Active learning accelerates electrolyte solvent screening for anode-free lithium metal batteries

Nature Communications
Anode-free or ‘zero-excess’ lithium metal batteries offer high energy density compared to current lithium-ion batteries but require electrolyte innovation to extend cycle life.
Peiyuan Ma, Ritesh Kumar, Ke-Hsin Wang, Chibueze V. Amanchukwu   +3 more
doaj   +1 more source

Bayesian active learning for multi‐objective feasible region identification in microwave devices

Electronics Letters, 2021
In microwave device and circuit design, many simulations are often needed to find a set of designs that satisfy one or multiple specifications chosen by the designer upfront: the feasible region. A novel Bayesian active learning framework is presented to
Federico Garbuglia, Jixiang Qing, Nicolas Knudde, Domenico Spina, Ivo Couckuyt, Dirk Deschrijver, Tom Dhaene   +6 more
doaj   +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

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

Active User Identification Based on Asynchronous Sparse Bayesian Learning With SVM

IEEE Access, 2019
In this paper, an asynchronous sparse Bayesian learning (ASBL) algorithm-based receiver for uplink (UL) grant-free transmission is proposed. The time-domain channel estimation is performed by the ASBL algorithm to obtain the channel impulse response (CIR)
Jingwei Fu, Gang Wu, Yizhong Zhang, Lijun Deng, Shu Fang   +4 more
doaj   +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

Deep active learning for multi label text classification

Scientific Reports
Given a set of labels, multi-label text classification (MLTC) aims to assign multiple relevant labels for a text. Recently, deep learning models get inspiring results in MLTC.
Qunbo Wang, Hangu Zhang, Wentao Zhang, Lin Dai, Yu Liang, Haobin Shi   +5 more
doaj   +1 more source

Bayesian optimization with active learning of design constraints using an entropy-based approach

npj Computational Materials, 2023
The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as
Danial Khatamsaz, Brent Vela, Prashant Singh, Duane D. Johnson, Douglas Allaire, Raymundo Arróyave   +5 more
doaj   +1 more source

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

Active Learning and Bayesian Optimization: A Unified Perspective to Learn with a Goal

Archives of Computational Methods in Engineering
AbstractScience and Engineering applications are typically associated with expensive optimization problem to identify optimal design solutions and states of the system of interest. Bayesian optimization and active learning compute surrogate models through efficient adaptive sampling schemes to assist and accelerate this search task toward a given ...
Di Fiore F., Nardelli M., Mainini L.
openaire   +5 more sources