Results 171 to 180 of about 20,199 (310)

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy   +8 more
wiley   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

open access: yesAdvanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath   +4 more
wiley   +1 more source

Reaction Environment Engineering for Selective C3+ Formation in CO2 Electroreduction: Progress and Perspectives

open access: yesAngewandte Chemie, EarlyView.
This work focuses on how to improve the selectivity and activity of electrocatalytic CO2 reduction to C3+ products, by the integration of electrocatalyst and electrolyte co‐design. We summarize key C3+ formation mechanisms and provide a comprehensive reaction network through thermodynamic analysis.
Ling Chen, Damien Voiry, Yan Jiao
wiley   +2 more sources

Autonomous AI‐Driven Design for Skin Product Formulations

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang   +5 more
wiley   +1 more source

The Role of Zn–Hf Site Proximity and Oxygen Vacancies for Methanol Formation Over ZnHfOx Catalysts Under CO2 Hydrogenation Conditions

open access: yesAngewandte Chemie, EarlyView.
ZnHfOx catalysts enable highly selective hydrogenation of CO2 to methanol over a wide range of Zn contents, as the Zn‐VO‐Hf active motif (VO = oxygen vacancy) exists not only in solid solution‐type ZnHfOx for low Zn contents (≤ 35 mol%), but also as HfOx cluster/single atom decorated ZnO surfaces for Zn contents > 95 mol%.
Alexander Oing   +8 more
wiley   +2 more sources

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

open access: yesAdvanced Intelligent Discovery, EarlyView.
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

Fusion Reactor Materials [PDF]

open access: yesEurophysics News, 1998
openaire   +1 more source

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