Results 221 to 230 of about 1,703,727 (282)

Model-agnostic linear-memory online learning in spiking neural networks. [PDF]

Nat Commun
Wang C, Dong X, Ji Z, Xiao M, Jiang J, Liu X, Huan Y, Wu S.   +7 more
europepmc   +1 more source

Safety of Sodium‐Ion Batteries: Evaluation and Perspective from Component Materials to Cells, Modules, and Packs

Advanced Energy Materials, EarlyView.
This review provides a bottom‐up evaluation of sodium‐ion battery safety, linking material degradation mechanisms, cell engineering parameters, and module/pack assembly. It emphasizes that understanding intrinsic material stability and establishing coordinated engineering control across hierarchical levels are vital for preventing degradation coupling ...
Won‐Gwang Lim, Seoa Kim, Erik A. Wu, Darren H. S. Tan, Shawn Peng, Xiaolin Li   +5 more
wiley   +1 more source

Fuzzy adaptive nonlinear MIMO control for rigid coupled multibody robots using reinforcement learning model. [PDF]

Sci Rep
Duan C, Wang L, Li S.
europepmc   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Noninvasive Assessment of the Ulnar Nerve in the Upper Extremity Using Machine Learning. [PDF]

J Hand Surg Glob Online
Dondapati A, Rodenhouse A, Carroll TJ, Lillie J, Anand A, Pink L, Mitten D, Ketonis C.   +7 more
europepmc   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

Correction: Multi-texture synthesis through signal responsive neural cellular automata. [PDF]

Sci Rep
Catrina MM, Plajer IC, Băicoianu A.
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Machine learning-driven model construction for automated classification of cognitive styles. [PDF]

Front Psychol
Wu X, Feng J, Ma J, Li Y, Sun J.
europepmc   +1 more source