Results 151 to 160 of about 545,363 (263)

MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning. [PDF]

Brief Bioinform
Xian L, Zou Q, Qi R, Niu M, Wang Y.
europepmc   +1 more source

Inversion of the Impedance Response Towards Physical Parameter Extraction Using Interpretable Machine Learning

Advanced Energy Materials, EarlyView.
ABSTRACT Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift‐diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible.
Mahmoud Nabil, Isel Grau, Ricardo Grau‐Crespo, Said Hamad, Juan A. Anta   +4 more
wiley   +1 more source

Enhancing intrusion detection in encrypted DoH traffic through a robust ensemble learning framework. [PDF]

PLoS One
Abrahim H, Hou W, Zhuang Y, Rahman HU.
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

Inversion of kiwifruit canopy nitrogen using UAV multispectral technology and ensemble learning. [PDF]

Front Plant Sci
Zhou B, Wang Y, Zhang J, Bai X, Tian M, Guo R.   +5 more
europepmc   +1 more source

Beyond Descriptor‐Based AI Design: Sp2‐Hybridized Branched Side Chains Enable Pre‐Aggregation–Driven Seeding Effects in Green‐Solvent‐Processed Organic Solar Cells

Advanced Energy Materials, EarlyView.
sp2‐hybridized branched side chains are introduced as a new molecular design for NFAs, YBOV, inducing strong solution‐state pre‐aggregation. This pre‐aggregation enables universal seeding motifs, highly ordered film growth, and overcoming the intrinsic current–voltage trade‐off, achieving 19.67% efficiency via green‐solvent processing beyond descriptor‐
Seokhwan Jeong, Donghoo Won, Zhe Sun, Chihyung Lee, Jaewook Kim, Seunglok Lee, Sangjin Yang, Jieun Kim, Keonho Yoon, Dong Young Kim, Yongjoon Cho, Seung‐Jae Shin, Hee‐Seung Lee, Doo‐Hyun Ko, Changduk Yang   +14 more
wiley   +1 more source

A hybrid stacked ensemble learning framework for multilabel text emotion detection. [PDF]

Sci Rep
Adamu H, Azmi Murad MA, Nasharuddin NA.
europepmc   +1 more source

Disorder‐Driven Fast Na+ Transport: From Crystalline to Amorphous Networks in the Mixed‐Anion NaTaOxCl6−2x Oxychlorides

Advanced Energy Materials, EarlyView.
Oxygen substitution in NaTaOxCl6‐2x drives structural evolution from isolated [TaCl6]– octahedra, through oxygen‐bridged [Ta2OCl10]2– dimers, toward extended trans‐[TaO2Cl4]3– chain‐like arrangements. At intermediate compositions, zero‐dimensional corner‐sharing motifs are proposed to create a flexible, disordered framework that peaks ionic ...
Justin Leifeld, Abigail C. Parsons, Alexandra Morscher, Xabier Martinez de Irujo‐Labalde, Marvin A. Kraft, Oskar G. Soulas, Bibek Samanta, Wiebke Zielasko, Neelam Yadav, Kyra Strotmann, Niina Jalarvo, Bruno V. M. Rodrigues, Adam Slabon, Philipp Adelhelm, Michael Ryan Hansen, David O. Scanlon, Wolfgang G. Zeier, Alexander G. Squires   +17 more
wiley   +1 more source

Prognostic Power of Ensemble Learning in Colorectal Cancer with Peritoneal Metastasis: A Multi-Institutional Analysis. [PDF]

Bioengineering (Basel)
Bamba Y, Itabashi M, Kobayashi H, Kotake K, Kawasaki M, Kanemitsu Y, Kinugasa Y, Ueno H, Maeda K, Suto T, Funahashi K, Ozawa H, Koyama F, Noura S, Ishida H, Ohue M, Kiyomatsu T, Ishihara S, Koda K, Baba H, Kawada K, Hashiguchi Y, Goi T, Toiyama Y, Tomita N, Sunami E, Akagi Y, Watanabe J, Hakamada K, Nakayama G, Sugihara K, Ajioka Y.   +31 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