Results 241 to 250 of about 106,398 (294)

Integrating Machine Learning With Constant‐Potential Simulation to Unravel Charge‐Transfer Mechanisms in Electrochemical Nitrogen Fixation

Advanced Science, EarlyView.
Integrating interpretable machine learning with the fixed‐potential method reveals a novel mechanism: the catalytic activity of the electrochemical nitrogen reduction reaction is governed by partial charge transfer, induced by variations in the intermediate potential of zero charge under constant potential.
Yufei Xue, Dushuo Feng, Yuefei Zhang, Yang Zhang, Yalong Jiao, Aijun Du, Guoping Gao   +6 more
wiley   +1 more source

Predicting food prices in Kenya using machine learning: a hybrid model approach with XGBoost and gradient boosting. [PDF]

Front Artif Intell
Ogol BO, Omondi E, Olukuru J, Muriithi B, Senagi K.   +4 more
europepmc   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting. [PDF]

BMC Bioinformatics
Dai C, Wang L, Deng Y, Gao X, Zhang J.
europepmc   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting. [PDF]

Sci Rep
Tang L, Huang L, Yuan Y.
europepmc   +1 more source

Harnessing Phase Separation for the Development of High‐Performance Hydrogels

Advanced Science, EarlyView.
ABSTRACT Hydrogels are indispensable for the development of next‐generation bioelectronics, soft robotics, and biomedical devices, where their mechanical properties determine performance and reliability. Among strategies to enhance hydrogel mechanics, phase separation enables controlled heterogeneity resulting in gel networks that are reinforced by ...
Yue Shao, Yiming Ma, Baihao Shao, Molly M. Stevens   +3 more
wiley   +1 more source

Identification of Protein-Protein Interaction (PPI) Sites on the Influenza A (H1N1) Viral Genome Using Gradient Boosting and Artificial Neural Network (ANN) Models. [PDF]

ACS Omega
Sajnani V, Ali O, Das S, Bandyopadhyay D.   +3 more
europepmc   +1 more source

Structural and Compositional Effects on the Scintillation Properties of Fast Emitting Metal‐Organic Frameworks

Advanced Science, EarlyView.
The crystalline structure, the atomic numbers of the constituent elementsand the size of MOF crystals each influence their photoluminescence and scintillation properties in distinct ways. Together, these factors provide valuable guidelines for designing systems with optimized performance and tunable timing characteristics, arising from the synergistic ...
Francesca Cova, Jacopo Perego, Biplab Joarder, Nobuhiro Yanai, Anna Vedda, Silvia Bracco, Angiolina Comotti, Angelo Monguzzi, Irene Villa   +8 more
wiley   +1 more source

Comparing gradient boosting and neural networks in the prediction of intersecting genes in gingival epithelial immunity.

J Indian Soc Periodontol
Ardila CM, Anegundi RV, Puttu G, Ayyachamy S, Yadalam PK.   +4 more
europepmc   +1 more source