Results 211 to 220 of about 155,735 (298)

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Explainable artificial intelligence-driven ensemble learning for asthma risk prediction using machine and deep learning. [PDF]

J Int Med Res
Druvo MMR, Islam A, Chowdhury A, Mohi Uddin KM.   +3 more
europepmc   +1 more source

A Highly Hydrophilic Air Electrode With Water‐Induced Surface Reconstruction for Efficient Reversible Protonic Ceramic Cell at Low Water Partial Pressures

Advanced Energy Materials, EarlyView.
This work demonstrates a new strategy for reversible protonic ceramic cells (R‐PCCs). By developing highly hydrophilic oxides, efficient operation is achieved under low water vapor pressures while maintaining high performance and stability. This approach addresses the challenge of hydrogen production in freshwater‐scarce regions.
Nai Shi, Yun Xie, Xueyu Hu, Junji Hyodo, Chun‐Kuo Peng, Gaopeng Zhou, Hanwen Liu, Bingbing Qiu, Daqin Guan, Jiayi Tang, Yan‐Gu Lin, Ranran Peng, Moses Oludayo Tadé, Meilin Liu, Yoshihiro Yamazaki, Zongping Shao   +15 more
wiley   +1 more source

Retraction Note: Enhancing blockchain transaction classification with ensemble learning approaches. [PDF]

Sci Rep
Panigrahi A, Pati A, Sahu B, Paul R, Nayak AK, Chowdhury S, Govindaraj R, Shreyas J.   +7 more
europepmc   +1 more source

Tracking Dynamic Sulfur Electrochemistry by Operando Techniques in Alkali Metal‐Sulfur Batteries

Advanced Energy Materials, EarlyView.
Dynamic sulfur electrochemistry in alkali metal‐sulfur batteries is tracked through operando spectroscopy, scattering, imaging, and modelling. This Review connects sulfur reaction pathways, polysulfide transport, electrolyte/interphase evolution, anode chemistry, and quantitative mechanistic analysis across Li‐S, Na‐S, and K‐S batteries, providing ...
Fangli Zhang, Meixuan Niu, Junya Huang, Dawei Su   +3 more
wiley   +1 more source

Explainable Ensemble Learning for Robust Severity Stratification of Carpal Tunnel Syndrome from Clinical Data. [PDF]

Diagnostics (Basel)
Sahin ME, Ulutas H, Korkmaz M, Ozbay Karakus M, Er O, Unluel H.   +5 more
europepmc   +1 more source

Do More Open Economies Export Less Diversely in Brazilian Agribusiness?

Agribusiness, EarlyView.
ABSTRACT This paper asks whether municipalities with a larger export‐to‐revenue ratio display a more concentrated bundle of exported goods in Brazilian agribusiness. Recent advances in export imputation for Brazilian municipalities allow for an analysis of the concentration profile of each municipality and its association with export openness.
Alan Leal, Michelle Marcia Viana Martins
wiley   +1 more source

Enhancing internet of things intrusion detection through high-performance boosting ensemble learning. [PDF]

Sci Rep
Yakovyna V, Fadieiev A.
europepmc   +1 more source

Dictionary‐based weak‐form training for noise‐robust series hybrid models with multiplicative unknowns

AIChE Journal, EarlyView.
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho, Akshay Kudva, Parth Shah, Dongheon Lee, Joseph Sang‐Il Kwon   +4 more
wiley   +1 more source

Ant Colony Optimization-Driven Ensemble Learning for Carbon Emission Modelling in Fly Ash-Slag Geopolymer Concrete. [PDF]

Materials (Basel)
Pandey IK, Kumar S, Prasad B, Kumar P, Ahmed M, Hussein AB.   +5 more
europepmc   +1 more source