Study of the Spontaneous Deamination of Cytosine by Computational and Conceptual DFT
Labet, Vanessa +4 more
openaire +3 more sources
Editorial Board Members' Collection Series: QSAR and Computational Approaches to Drug Discovery. [PDF]
Torrens F, de Julián-Ortiz JV.
europepmc +1 more source
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su +13 more
wiley +1 more source
Anion-controlled ion pairing and assembly of π-electronic cations with orthogonal π-systems. [PDF]
Haketa Y +11 more
europepmc +1 more source
Sliding Ferroelectricity Driven Spin‐Layertronics in Altermagnetic Multilayers
Integrating sliding ferroelectricity with altermagnetism enables nonvolatile electrical control of spin and layer degrees of freedom. In bilayer CuF2, interlayer translation reverses layer‐locked spin‐split bands, establishing a multifunctional “spin‐layertronic” platform.
Rui Peng +5 more
wiley +1 more source
Carboxylate-Ligand-Based Coordination Polymers and Metal Complexes: From Structural Diversity and Supramolecular Descriptors to Function-Oriented Design. [PDF]
Kong X, Wang X, Tai X.
europepmc +1 more source
Dual‐Module Near‐Infrared Fluorophores Discovery System via Knowledge Transfer
This study presents a dual‐module deep learning system for the design of near‐infrared (NIR) fluorophores. A large molecular library is generated and analyzed, leading to the suggestions of promising candidates. The effectiveness of the system is further validated through the synthesis, characterization, and in vivo imaging, demonstrating its potential
Yixin Zhu +7 more
wiley +1 more source
Mechanically Interlocked Molecules: A Supramolecular Computational Picture of Rotaxane-Based Switches, Shuttles, and Tristable Devices in Solution. [PDF]
Zazza C.
europepmc +1 more source
Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai +3 more
wiley +1 more source
Taming conformational entropy for low-cost and high-performance organic photovoltaics. [PDF]
Chen Y.
europepmc +1 more source

