Results 201 to 210 of about 285,439 (304)

Causal K-Means Clustering. [PDF]

open access: yesJ R Stat Soc Series B Stat Methodol
Kim K, Kim J, Kennedy EH.
europepmc   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

open access: yesAdvanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin   +7 more
wiley   +1 more source

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

open access: yesAdvanced 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   +5 more
wiley   +1 more source

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

open access: yesAdvanced 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   +6 more
wiley   +1 more source

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

open access: yesAdvanced 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   +19 more
wiley   +1 more source

Temperature Sensitivity and Adaptation of Cereal Yields: Empirical Evidence From Italy

open access: yesApplied Economic Perspectives and Policy, EarlyView.
ABSTRACT This paper investigates the evolving temperature sensitivity and climate adaptation of cereal yields in Italy from 1952 to 2023, using province‐level data for maize, common wheat, and durum wheat. Employing panel data econometric methods, we estimate yield responses to heat exposure, changes in sensitivity over time, and adaptation to climate ...
Paolo Nota   +2 more
wiley   +1 more source

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