Quantum-informed machine learning for predicting spatiotemporal chaos with practical quantum advantage. [PDF]
Wang M, Xue X, Gao M, Coveney PV.
europepmc +1 more source
Diffusion–Model–Driven Discovery of Ferroelectrics for Photocurrent Applications
We developed a diffusion model–based generative AI and high‐throughput screening framework that accelerates the discovery of photovoltaic ferroelectrics. By coupling AI driven crystal generation with machine learning and DFT screening, we identified Ca3P2 and LiCdP as new ferroelectric materials exhibiting strong polarization, feasible switching ...
Byung Chul Yeo +3 more
wiley +1 more source
QRGEC: quantum reinforcement learning with golden jackal optimization for resilient edge cloud coordination in internet computing. [PDF]
Lella KK, Krishna MSR.
europepmc +1 more source
Electrolyte Additive Strategies in Aqueous Zn‐Ion Batteries: Recent Advances and Prospects
This article provides a comprehensive overview of the current status and future development directions of AZIBs electrolyte additives in three aspects: stabilizing zinc anodes (uniform deposition, inhibition of dendritic crystals), protecting cathodes (structural stability, inhibition of dissolution), and enhancing electrolyte stability (wider ...
Yuanze Yu +7 more
wiley +1 more source
Quantum transfer learning for cross-domain cybersecurity threat detection and categorization. [PDF]
Alsubai S +6 more
europepmc +1 more source
This review surveys nanoparticle‐based strategies to enhance adoptive cell therapy, particularly CAR‐T cell approaches, in solid tumor treatment. It describes how nanoparticles can improve tumor immunogenicity and T‐cell infiltration while reducing toxicity, and how they enable in vivo CAR‐T cell generation.
Erica Frostegård +19 more
wiley +1 more source
A novel quantum convolutional neural network framework for quantum-enhanced classification of pixelated colour images. [PDF]
Daka C, Bhattacharyya S.
europepmc +1 more source
Generative AI‐Driven Accelerated Discovery of Passivation Molecules for Perovskite Solar Cells
A generative artificial intelligence (AI) framework combining a discriminative machine learning model (SMILES‐X) and a generative language model (GPT‐2) autonomously discovers new molecular passivators for perovskite solar cells (PSCs). Through an iterative design loop, over 100 000 candidates are generated and screened, and randomly selected molecules
Adroit T. N. Fajar +7 more
wiley +1 more source
Few-shot android malware classification with quantum-enhanced prototypical learning and drift detection. [PDF]
Tawfik M +5 more
europepmc +1 more source
ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals
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 +3 more
wiley +1 more source

