Results 71 to 80 of about 6,622 (258)

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Machine‐Learning Framework for Designing Stable Interfaces in All‐Solid‐State Lithium‐Ion Batteries

Advanced Science, EarlyView.
A data‐driven strategy is developed to discover coating materials for all‐solid‐state lithium batteries. Using calculations of interfacial reactivity, unsupervised pattern recognition, and machine‐learning prediction, the study identifies low‐reactivity compositional patterns and screens new lithium‐based oxide and polyanion candidates, extending ...
Sehyeok Park, Myeongcho Jang, Hun‐Gi Jung, Kyung Yoon Chung, Seungho Yu   +4 more
wiley   +1 more source

A decomposition clustering ensemble learning approach for forecasting foreign exchange rates

Journal of Management Science and Engineering, 2019
A decomposition clustering ensemble (DCE) learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition (VMD), the self-organizing map (SOM) network, and the kernel extreme learning machine (KELM).
Yunjie Wei, Shaolong Sun, Jian Ma, Shouyang Wang, Kin Keung Lai   +4 more
doaj   +1 more source

Resolving Oxidative and Corrosive Calendar‐Aging via Electrolyte Engineering for Stable Lithium Metal Batteries

Advanced Energy Materials, EarlyView.
A weakly solvating ether solvent, 1,2‐dimethoxypropane (DMP), is proposed for use in localized high‐concentration electrolytes (LHCEs) for lithium metal batteries (LMBs). These DMP‐based LHCEs simultaneously suppress lithium metal corrosion and cathode degradation—two interrelated processes that accelerate calendar aging of LMBs.
Jisub Kim, Ihnkyung Jung, Kyunam Lee, Insu Hwang, Minkwan Kim, Eunseok Park, Dongmin Park, Hayoung Park, Yonggoon Jeon, Gaeun Park, Hyo Chul Ahn, Jihoon Oh, Jungwon Park, Ali Coskun, Jang Wook Choi   +14 more
wiley   +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

Bird Call Identification Using Ensemble Empirical Mode Decomposition

Shock and Vibration
Birds are iconic species of the environment. Bird monitoring can be achieved by collecting recordings of the calls of wild birds and later identifying the species. A new approach suggested in this study involves the application of ensemble empirical mode
Jingxuan Liu, Hailan Chen
doaj   +1 more source

Quantification of Dynamic Properties of Pile Using Ensemble Empirical Mode Decomposition

Advances in Civil Engineering, 2018
This paper investigated dynamical interactions between pile and frozen ground by using the ensemble empirical mode decomposition (EEMD) method. Unlike the conventional empirical mode decomposition (EMD) method, EEMD is found to be able to separate the ...
Feng Xiao, Gang S. Chen, Wael Zatar, J. Leroy Hulsey   +3 more
doaj   +1 more source

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

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Automatic Interference Term Retrieval From Spectral Domain Low-Coherence Interferometry Using the EEMD-EMD-Based Method

IEEE Photonics Journal, 2016
Low-coherence interferometry (LCI) has proved to be a useful tool in optical measurement and detection. However, the noise that is present in practical applications makes interference term retrieval (ITR) difficult.
Hongxia Zhang, Feng Wang, Dagong Jia, Tiegen Liu, Yimo Zhang   +4 more
doaj   +1 more source