Results 141 to 150 of about 201,035 (291)

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

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

Implantation‐On‐Chip: An AI‐Based Platform for Monitoring the Embryo Trophoblast–Endometrial Stroma Cross Talk With Xenobiotics Interference

open access: yesAdvanced Intelligent Systems, EarlyView.
We present a novel AI‐integrated implantation‐on‐chip platform that enables mimicking and monitoring the maternal–fetal interactions at the early phases of human embryo implantation with high spatiotemporal resolution. The complexity of the trophoblast invasion process was addressed by conducting the analysis at global (rate of invasion) and local ...
Joanna Filippi   +12 more
wiley   +1 more source

Deep Learning Approaches for Classifying Crack States With Overload and Predicting Fatigue Parameters in a Titanium Alloy

open access: yesAdvanced Intelligent Systems, EarlyView.
This study proposes a deep learning approach to evaluate the fatigue crack behavior in metals under overload conditions. Using digital image correlation to capture the strain near crack tips, convolutional neural networks classify crack states as normal, overload, or recovery, and accurately predict fatigue parameters.
Seon Du Choi   +5 more
wiley   +1 more source

Thermal neutron transmutation doping of GaN semiconductors. [PDF]

open access: yesSci Rep, 2020
Barber R   +4 more
europepmc   +1 more source

Predicting Crystal Structures and Ionic Conductivities in Li3YCl6−xBrx Halide Solid Electrolytes Using a Fine‐Tuned Machine Learning Interatomic Potential

open access: yesAdvanced Intelligent Systems, EarlyView.
This study refines the Crystal Hamiltonian Graph Network to predict energies, structures, and lithium‐ion dynamics in halide electrolytes. By generating ordered structural models and using an iterative fine‐tuning workflow, we achieve near‐ab initio accuracy for phase stability and ionic transport predictions.
Jonas Böhm, Aurélie Champagne
wiley   +1 more source

Research on Evaporation Characteristics and Safe Storage Duration of Marine Liquid Hydrogen Storage Tanks Under the Coupling Effect of Heat Leakage and Sloshing

open access: yesAsia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT Liquid hydrogen, a zero‐carbon and high–energy‐density fuel, is a promising option for future oceangoing vessels. During maritime transportation, onboard cryogenic tanks are exposed to ambient heat leakage and ship‐induced roll motion, which can trigger sloshing and fundamentally modify the coupled thermo‐fluid processes governing boil‐off and
Yan Deng   +5 more
wiley   +1 more source

Optimization study of a transportable neutron radiography system based on a 252CF neutron source

open access: yesMoldavian Journal of the Physical Sciences, 2011
The purpose of this work is the optimization of a transportable thermal neutron radiography system. Neutrons are produced by a 50 mg 252Cf source.
Fantidis, J.   +3 more
doaj  

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