Results 171 to 180 of about 147,000 (286)

Data-driven p-norms for estimating transmission loss coefficients in power systems. [PDF]

PLoS One
Montoya OD, Gil-González W, Grisales-Noreña LF.   +2 more
europepmc   +1 more source

FIRE‐GNN: Force‐Informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties

Advanced Intelligent Discovery, EarlyView.
This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu, Claire Schlesinger, Karan Mudaliar, Jordan Leung, Robin Walters, Peter Schindler   +5 more
wiley   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Short-term forecasting and impact analysis of COVID-19 and the stock market in Morocco using ARIMA. [PDF]

PLoS One
Aboagye N, Nadarajah S.
europepmc   +1 more source

Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Advanced Intelligent Discovery, EarlyView.
Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Performance Analysis of Boosting-Based Machine Learning Models for Predicting the Compressive Strength of Biochar-Cementitious Composites. [PDF]

Materials (Basel)
Kim J, Ryu D, Hwan H, Lee H.
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Molecule by Molecule Characterization of a Polymer Molecular Mass Distribution via Mass Photometry

Angewandte Chemie, EarlyView.
Mass photometry (MP) was utilized to achieve label‐free, molecule‐by‐molecule measurement of the full polymer molecular mass distribution (MMD) of a synthetic polymer without the need for sample fractionation. The ability to elucidate the full MMD in minutes, as opposed to having only specific moments of the MMD acquired in hours via chromatography ...
Rachel Czerwinski, Anna L. Clayborn, Aisley Fleming, Andrew J. Boydston, Aaron H. Hoskins, Randall H. Goldsmith   +5 more
wiley   +2 more sources

Ensemble Entropy with Adaptive Deep Fusion for Short-Term Power Load Forecasting. [PDF]

Entropy (Basel)
Wang Y, Niu Y, Li X, Dai X, Wang X, Jiang Y, He C, Zhou L.   +7 more
europepmc   +1 more source