Results 181 to 190 of about 9,060,306 (295)
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Measuring the Hall Effect in Hysteretic Materials
The authors highlight common pitfalls in measuring the Hall effect: in hysteretic magnets, improper data processing can create signals that look exotic but are not real. This Perspective explains the origin of these artifacts and presents practical measurement strategies that help researchers identify reliable Hall responses in complex magnetic ...
Jaime M. Moya +6 more
wiley +1 more source
ABSTRACT Accurately knowing the frontier orbital energies of the structurally disordered small‐molecule organic semiconductors that are used in optoelectronic devices such as organic light‐emitting diodes is required to rationally improve their performance. Here, we show that these energies can be deduced with a large accuracy from the peak energies of
Christian B. McDonald +7 more
wiley +1 more source
Radiative Thermal Management for Extreme Environments: Mechanisms and Design Strategies
In this review, we discuss the fundamental mechanisms of radiative cooling and solar heating, material and structural design strategies, hybrid approaches integrating additional heat transfer pathways, and durability‐oriented designs addressing environmental stresses.
Hyung Rae Kim +5 more
wiley +1 more source
Inverse Design of Nanoparticulate Materials
Inverse design shifts nanomaterial development from empirical trial‐and‐error to predictive model‐driven strategies. It can rely on knowledge‐based, data‐based, or hybrid process and property functions. This perspective article provides a practical framework for applying inverse design based on instructive examples. It discusses which modeling approach
Nabi Etienne Traoré +5 more
wiley +1 more source
Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models
Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng +24 more
wiley +1 more source
A Critical Call for Mechanistic Investigations of Metal‐Bacteria Interactions
It is well established that the antibacterial properties of metals have tremendous importance for bioengineering applications. What is not as appreciated, however, is the reproducibility crisis in this field's vast literature. In this perspective, we highlight this reproducibility problem, discuss its sources, and suggest solutions towards focused ...
J. Rackwitz +9 more
wiley +1 more source
Biomass DNA: An Emerging Building Block for Advanced Materials
Biomass DNA is rapidly emerging from its traditional role as a genetic blueprint to serve as a versatile abundant, highly programmable polymeric building block. This review highlights the transformation of macroscopic bulk biomass DNA into next generation of functional materials, spanning from dynamic hydrogels, sustainable bioplastics, and high ...
Yuwei Hu, Itamar Willner, Li Niu
wiley +1 more source
Understanding Operando Water Management in Hydroxide‐Exchange‐Membrane Fuel Cells
Effective water management is vital for high‐performance hydroxide‐exchange‐membrane fuel cells. Using a custom water‐flux station, this study quantifies how membrane thickness, microporous layers, and operating conditions dictate internal water transport.
Catherine M. Weiss +4 more
wiley +1 more source

