Results 191 to 200 of about 3,120,690 (293)

Dynamic Landau–Khalatnikov Modeling of Ferroelectric Hafnium–Zirconium Oxides Across Volatile and Non‐Volatile Switching

Advanced Electronic Materials, EarlyView.
Compositionally engineered hafnium–zirconium oxide capacitors are described by an extended Landau–Khalatnikov framework incorporating inertial and nonlinear damping terms. The unified model captures ferroelectric and volatile regimes within a single material platform, linking free‐energy landscapes to circuit‐level behavior.
Davide Rossetti, Yichun Xu, Shiva Asapu, Taehwan Moon, Seung Ju Kim, Drake R. Austin, Ruoyu Zhao, Tong Wang, Jian Zhao, Brian M. Everhart, Michele Bonnin, Alon Ascoli, Fernando Corinto, Nicholas R. Glavin, R. Stanley Williams, J. Joshua Yang   +15 more
wiley   +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

Bayesian networks for network inference in biology. [PDF]

J R Soc Interface
Hammond J, Smith VA.
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

Dynamic Bayesian networks to predict loss of kidney function: a cross-institution use case in a large cohort with or at-risk of CKD. [PDF]

BMC Med Inform Decis Mak
Petousis P, Gordon D, Duru OK, Norris KC, Tuttle KR, Nicholas SB, Bui AAT.   +6 more
europepmc   +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

Identifying risk factors and predicting stroke using Bayesian networks: Evidence from NHANES 2011-2020. [PDF]

Digit Health
Zhao J, Zhang M, Wang H.
europepmc   +1 more source

Farmers' Preferences for Gene Editing Crops and Influencing Factors

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Gene editing (GE) is gaining momentum worldwide, but limited data on UK farmers' preferences hinders our understanding of its potential impact amid deregulation debates. Based on a survey of 200 English arable farmers, we employ a Latent Class Analysis and Multinomial Logit regressions to investigate current preferences for GE crops.
Bertolozzi‐Caredio Daniele, Vigani Mauro   +1 more
wiley   +1 more source

Modeling variable interactions using Bayesian networks to identify direct predictors of radiation-induced optic neuropathy after proton therapy: implications for personalized toxicity risk stratification. [PDF]

Phys Med Biol
Pham TN, Chamseddine I, Lebhertz D, Quintyn JC, Paganetti H, Thariat J.   +5 more
europepmc   +1 more source

Differentiated Impacts of EU Rural Development Measures: A Finite Mixture Evaluation of Italian Olive Farms

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT This paper examines the relationship between participation in the EU Rural Development Program and the economic performance of Italian olive farms using a finite‐mixture model with inverse‐probability‐weighted regression adjustment. Based on 2010–2022 FADN panel data, it estimates heterogeneous treatment effects while correcting for selection ...
Francesco Caracciolo, Marilena Furno
wiley   +1 more source