Results 161 to 170 of about 45,420 (307)

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

Advanced 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, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Examining Potential Boundary Bias Effects in Kernel Smoothing on Equating: An Introduction for the Adaptive and Epanechnikov Kernels. [PDF]

Appl Psychol Meas, 2015
Cid JA, von Davier AA.
europepmc   +1 more source

Sustainable Carbon Fibers Enable Stable Long‐Term Lithium Metal Deposition for Prospective Zero‐Excess Lithium Metal Batteries

Advanced Energy Materials, EarlyView.
This work presents lightweight, lignin‐derived carbon fiber current collectors that enable controlled lithium deposition. Structural defects and intermediate‐sized pores stabilize pre‐nucleation quasi‐metallic lithium clusters, promoting uniform lithium plating and stripping.
Samantha L. S. Southern, Jonathon Cottom, Jana B. Fritzke, Emma N. Antonio, Zhenyu Guo, Bhavini Patel, Romain Tort, Ifan E. L. Stephens, Camilla Di Mino, Clare P. Grey, Rhodri Jervis, Emilia Olsson, Heather Au, Maria‐Magdalena Titirici   +13 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

NONPARAMETRIC KERNEL ESTIMATION OF MULTIPLE HEDGE RATIOS

It is possible for the traditional hedge ratio estimation to produce erroneous guidance to risk managers because of the restrictive assumptions. This study adopts nonparametric locally polynomial kernel estimation to exclude the assumptions. Results from
Kim, MinKyoung, Leuthold, Raymond M.
core  

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

Sustainable Productivity Growth in Agriculture: The Role of Shifts in R&D Investments and Technology

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT The objective of the paper is to evaluate the long‐term prospects of sustainable productivity growth linked to plausible assumptions on public agricultural R&D investments as the key productivity driver. Second, it investigates the role of changing R&D focus from yield maximization to input saving technologies (fertilizers and pesticides). The
Zuzana Smeets Křístková, Michiel van Dijk, Robert M'Barek, Hans van Meijl, Tomas Ratinger   +4 more
wiley   +1 more source

Oracally Efficient Two-Step Estimation of Generalized Additive Model [PDF]

Generalized additive models (GAM) are multivariate nonparametric regressions for non-Gaussian responses including binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions.
Wolfgang Karl Härdle, Rong Liu, Lijian Yang   +2 more
core  

Kernel Smoothing in Semiparametric Regression

A semiparametric regression model consists of parametric explanatory part of the response as well as nonparametric regression function of one or more variable(s) interpreting the response. The basic semiparametric regression model involves a linear function of a single parametric covariate as well as an unknown but preferably nonlinear function of a ...
openaire   +1 more source

Understanding Egg Price Volatility and Policy Implications in the U.S. With Machine Learning

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Eggs are an inexpensive and sustainable source of proteins, but volatility in the U.S. egg prices has intensified in recent years, raising concerns over food affordability and market stability. This study examines the drivers of U.S. egg price dynamics over 2004–2025 using a two‐stage framework that combines LASSO‐based variable selection with
Xuemei Zhao, Simon Meister, Lucie Maruejols, Xiaohua Yu   +3 more
wiley   +1 more source