Results 211 to 220 of about 49,141 (264)

Machine learning potentials for modeling alloys across compositions. [PDF]

Sci Adv
Sheriff K, Xiao DZ, Cao Y, Owen LR, Freitas R.   +4 more
europepmc   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

Geometry‐Based Neural‐Network Prediction of Electron Localization Function Topology in Dense Hydrogen

Chemistry – A European Journal, EarlyView.
We present a machine‐learning framework that predicts the electron localization function (ELF) of dense hydrogen directly from atomic geometry, bypassing explicit electronic‐structure calculations. Trained on ab initio data for fluid hydrogen across multiple pressures, the model achieves high accuracy and reveals pressure‐dependent nonlocal ...
Xiaoyu Wang, Miriam Marqués, Sergio Gómez, Francesc Serratosa, Eva Zurek, Julia Contreras‐García   +5 more
wiley   +1 more source

Non-destructive yield estimation of onion and garlic using UAV-based hyperspectral imaging and hybrid machine learning models. [PDF]

BMC Plant Biol
Su Y, Long J, Chen J, Zhou R, Liu Z, Deng H, Yuan Y.   +6 more
europepmc   +1 more source

Hybrid machine learning and genetic algorithm approach for catalyst and process optimization in Fischer–Tropsch synthesis toward sustainable fuel production

The Canadian Journal of Chemical Engineering, EarlyView.
Graphical representation of a data‐driven framework for Fischer‐Tropsch synthesis (FTS) modelling and optimization. Abstract This study presents a data‐driven approach for predicting the relationships between catalyst design, process conditions, and product selectivity in Fischer–Tropsch synthesis (FTS).
Doaa M. Hassan, Ryan Gosselin, Nicolas Abatzoglou   +2 more
wiley   +1 more source

Improved prediction of childhood anemia using hybrid ensemble learning and dual-level explainability. [PDF]

J Public Health Res
Kukkar A, Loibor JM, Cifci MA, Barman MP, Hussain S, Gaftandzhieva S, Gaftandzhieva S, Doneva R.   +7 more
europepmc   +1 more source

Classification of "Athlete's Heart" using machine learning of conventional 12-lead ECG: male elite 3,000-m runner data in the CHIEF study. [PDF]

Front Sports Act Living
Fan CH, Chen CF, Huang WC, Kwon Y, Sui X, Lavie CJ, Lin GM.   +6 more
europepmc   +1 more source

AI-driven vibration-based event classification in railway switches and crossings. [PDF]

Sci Rep
Amin MA, Najeh T, Ghoul A.
europepmc   +1 more source

GFI-MLP

openaire   +2 more sources

DeiT-MLP-mixer for the preoperative prediction of axillary lymph node involvement in breast cancer via ultrasound imaging. [PDF]

BMC Med Imaging
Asadoorian N, Yaraghi S, Tahmasian A, Fatehifard R, Rezazadegan M, Mottaghi N.   +5 more
europepmc   +1 more source