Results 191 to 200 of about 23,653 (255)
A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam +5 more
wiley +1 more source
Quantum machine learning enhanced laser speckle analysis for precise speed prediction. [PDF]
Chen Y +5 more
europepmc +1 more source
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley +1 more source
Overcoming the coherence time barrier in quantum machine learning on temporal data. [PDF]
Hu F +6 more
europepmc +1 more source
Advancements in Graphdiyne‐Based Multiscale Catalysts for Green Hydrogen Energy Conversion
This review systematically explores the fundamental characteristics of graphdiyne (GDY), cutting‐edge field of GDY‐based multiscale catalysts within sustainable energy conversion systems.Special emphasis is placed on the structure‒property relationships in different reactions.
Qian Xiao, Lu Qi, Siao Chen, Yurui Xue
wiley +1 more source
Deep Ensemble learning and quantum machine learning approach for Alzheimer's disease detection. [PDF]
Jenber Belay A, Walle YM, Haile MB.
europepmc +1 more source
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
Transition role of entangled data in quantum machine learning. [PDF]
Wang X +5 more
europepmc +1 more source
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley +1 more source
Publisher Correction: Overcoming the coherence time barrier in quantum machine learning on temporal data. [PDF]
Hu F +6 more
europepmc +1 more source

