Results 271 to 280 of about 110,828 (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

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

Advanced 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, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 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

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

Disorder‐Driven Ionic Mobility Edge and Localization‐Driven Dendrite Formation in Solid Electrolytes

Advanced Energy Materials, EarlyView.
Migration‐barrier disorder localizes ionic transport into a sparse percolation backbone. Current and electric‐field focusing at filament tips promote fractal dendrite growth and suppress the critical current density according to jcrit = j0 exp(−σEeff/kBT).
Dongwook Lee, Jiwon Seo
wiley   +1 more source

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Bagging for Gaussian process regression

Neurocomputing, 2009
This paper proposes the application of bagging to obtain more robust and accurate predictions using Gaussian process regression models. The training data are re-sampled using the bootstrap method to form several training sets, from which multiple Gaussian process models are developed and combined through weighting to provide predictions.
Tao Chen 0009, Jianghong Ren
openaire   +3 more sources

Bounded Gaussian process regression

2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework.
Bjørn Sand Jensen, Jens Brehm Nielsen, Jan Larsen   +2 more
openaire   +1 more source

Recursive Gaussian process regression

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
For large data sets, performing Gaussian process regression is computationally demanding or even intractable. If data can be processed sequentially, the recursive regression method proposed in this paper allows incorporating new data with constant computation time.
openaire   +1 more source

Divisive Gaussian Processes for Nonstationary Regression

IEEE Transactions on Neural Networks and Learning Systems, 2014
Standard Gaussian process regression (GPR) assumes constant noise power throughout the input space and stationarity when combined with the squared exponential covariance function. This can be unrealistic and too restrictive for many real-world problems.
Luis Munoz-Gonzalez, Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal   +2 more
openaire   +2 more sources

Distributed robust Gaussian Process regression

Knowledge and Information Systems, 2017
We study distributed and robust Gaussian Processes where robustness is introduced by a Gaussian Process prior on the function values combined with a Student-t likelihood. The posterior distribution is approximated by a Laplace Approximation, and together with concepts from Bayesian Committee Machines, we efficiently distribute the computations and ...
Sebastian Mair 0001, Ulf Brefeld
openaire   +2 more sources