Results 101 to 110 of about 147,708 (238)
Reply to Fernández-Quevedo García et al.: Surface tension in phase-separated active Brownian particles. [PDF]
Li L, Sun Z, Yang M.
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
Current interest in artificial cell research underscores its potential to deepen our understanding of life's fundamental processes. This review highlights advances in bottom‐up coacervate‐based artificial cell engineering via combined integration of cellular hallmarks.
Arjan Hazegh Nikroo +3 more
wiley +2 more sources
Programmable π-electron quantum matter: from molecular qubits to correlated and topological states. [PDF]
Chi L.
europepmc +1 more source
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto +3 more
wiley +1 more source
This work details the rapid generation (t ≤ 5 min) of size‐tunable, ultralow dispersity (Ð ≤ 1.01) 2D hexagonal nanosheets by self‐limiting polymerization‐induced crystallization‐driven self‐assembly (SL‐PI‐CDSA) of modular and templating poly(aryl isocyanide) block copolymers, with functions that permit post‐polymerization modifications. Specifically,
Randall A. Scanga +13 more
wiley +2 more sources
Preface: Celebrating the work and achievements of Joachim Stöhr. [PDF]
Eisebitt S, Dürr H, Lüning J.
europepmc +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
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
An equilibrium rotator glass-forming phase for long-ranged repulsive colloidal rods. [PDF]
Besseling TH +5 more
europepmc +1 more source
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary +1 more
wiley +1 more source

