Results 221 to 230 of about 9,031,100 (302)

Retraction Note: Enhancing blockchain transaction classification with ensemble learning approaches. [PDF]

Sci Rep
Panigrahi A, Pati A, Sahu B, Paul R, Nayak AK, Chowdhury S, Govindaraj R, Shreyas J.   +7 more
europepmc   +1 more source

The Importance of Metal‐Organic Framework Linker Atoms for CO2 Reduction: A DFT Study

Advanced Science, EarlyView.
Using DFT, we examine the role of linker atoms in CO2 reduction on copper‐based metal organic frameworks (Cu MOFs). Our calculations reveal that linker atoms may serve as both CO2 and H‐shuttling sites and suggest linker electrostatics as a descriptor for linker activity. ABSTRACT Although the metal within the secondary building unit of a metal‐organic
Ugochukwu Nwosu, Samira Siahrostami
wiley   +1 more source

MoleRate: comparing molecular relative evolutionary rates to detect convergent evolution. [PDF]

Evolution
Selberg AG, Clark N, Nekrutenko A, Chikina M, Kosakovsky Pond SL.   +4 more
europepmc   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Comment on "Symptom Clusters and Frailty Among Older Adults With and Without Cancer: Insights From Two Cross-Sectional Studies". [PDF]

Nurs Health Sci
Desai S, Malik MMUD.
europepmc   +1 more source

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Advanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +1 more source

Space-time-coding metasurfaces for high-dimensional communications with OAM-, polarization-, and frequency-division multiplexing. [PDF]

Light Sci Appl
Zhang L, Cui TJ.
europepmc   +1 more source

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

Spin-Resolved Dielectric Bath Embedding Theory for Metallic Surfaces. [PDF]

J Chem Theory Comput
Lee K, Fawcett C, Zhao Q.
europepmc   +1 more source

High‐Throughput Data Generation and Transfer Learning Enabled Microstructure‐Property Integrated Design of Nickel‐Based Powder Metallurgy Superalloy

Advanced Science, EarlyView.
An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li, Hongtao Zhang, Zichao Peng, Yuheng Zhang, Qian Lv, Yihuan Cao, Xuqing Wang, Huadong Fu, Jianxin Xie   +8 more
wiley   +1 more source