Results 161 to 170 of about 4,990,305 (257)

Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction

Advanced Intelligent Discovery, EarlyView.
Multimodal fusion integrates composition, crystal‐structure, and radial‐distribution descriptors to predict battery discharge capacity. Rashomon analysis across near‐optimal models reveals that explanatory variation is structured rather than arbitrary, separating stable mechanistic signals from model‐contingent attributions and providing a more ...
Jue Gong, Lejia Wang, Tianqing Zhu, Keith Butler, Sichao Li   +4 more
wiley   +1 more source

Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network. [PDF]

BMC Microbiol
Dissanayake D, Brunner V, Adlard D, Morrone JA, Fowler PW.   +4 more
europepmc   +1 more source

Materials Representation Learning Based on a Material–Motif Network and Heterogeneous Graphs

Advanced Intelligent Discovery, EarlyView.
Structure motifs in materials are used to construct a bipartite material–motif network that links each material to its constituent motifs and establishes connectivity among materials sharing common motifs. Network analysis reveals material clusters associated with different functional applications and supports motif‐guided screening of materials.
Anoj Aryal, Weiyi Gong, Huta Banjade, Qimin Yan   +3 more
wiley   +1 more source

ModelistsGCN: a multimodal graph convolutional network framework for single-cell spatial transcriptomic cell typing. [PDF]

Brief Bioinform
Konforti N, Goldberg T, Danino-Levi M, Ilan Y, Alon S.   +4 more
europepmc   +1 more source

Distributed Optimization of Graph Convolutional Network Using Subgraph Variance

Distributed Optimization of Graph Convolutional Network Using Subgraph ...
Taige Zhao (13101213), Jianxin Li (13075998), Wei Luo (13099695), Man Li (81239), Imran Razzak (13074405), Xiangyu Song (13095639)   +5 more
core  

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

iGraphCTC: an inter-connected graph convolutional network for comprehensive clinical trial collaborations. [PDF]

Sci Rep
Jang J, Ahn H, Park E.
europepmc   +1 more source

Role-Play Prediction using Ontology-Based Graph Convolutional Network Model

Role-Play Prediction using Ontology-Based Graph Convolutional Network ...
Masanori Fukui (21488471), Siti Khomsah (21488468), Asyafa Ditra Hauna (21488465), Andi Prademon Yunus (19944702), Yit Hong Choo (16683996)   +4 more
core  

Catalyst‐Specialized Chemical Language Model Based on Transformer Variational Autoencoder for Catalyst Design and Discovery

Advanced Intelligent Discovery, EarlyView.
We present CatTransVAE, a catalyst‐specialized chemical language model (CLM) built on a transformer variational autoencoder (VAE), developed through pretraining on general compounds followed by fine‐tuning on diverse catalyst databases. A template‐guided generation framework is introduced to enable controlled catalyst design under structural ...
Apakorn Kengkanna, Masahito Ohue
wiley   +1 more source

Microclimate prediction for sandy photovoltaic power plants using a spatio-temporal graph convolutional network with environmental covariates. [PDF]

iScience
Li J, Yang Z, Wang W, Li H, Dong W, Zhou B, Liu M, Li M.   +7 more
europepmc   +1 more source