Results 201 to 210 of about 25,210 (289)

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Fabrication Process and Surface Morphology Prediction of Radial Straight Groove-Structured CBN Grinding Wheel by Laser Cladding. [PDF]

Materials (Basel)
Ma Z, Zhang W, Liu Q, Chen L, Zhang C, Liu C, Yu T, Wang Q.   +7 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

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

Advanced 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, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Parameter Optimisation of Johnson-Cook Constitutive Models for Single Abrasive Grain Micro-Cutting Simulation: A Novel Methodology Based on Lateral Material Displacement Analysis. [PDF]

Materials (Basel)
Rypina Ł, Lipiński D, Tomkowski R.
europepmc   +1 more source

Smart Denture with Embedded Thin‐Film Temperature Sensors Patterned by Femtosecond Laser Pulse for Elderly Healthcare

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
We demonstrate the direct‐laser patterning of a gold thin film on polymethyl methacrylate to fabricate a temperature sensor for dentures. The temperature sensor‐embedded smart dentures are evaluated in an oral environment, enabling in‐situ monitoring for elderly healthcare.
Han Ku Nam, Yong Kwon Chae, Seoung‐Jin Hong, Younggen Lee, Dongwook Yang, Byunggi Kim, Sang Wook Kang, Young‐Jin Kim   +7 more
wiley   +1 more source

Study on the Low-Damage Material Removal Mechanism of Silicon Carbide Ceramics Under Longitudinal-Torsional Ultrasonic Grinding Conditions. [PDF]

Micromachines (Basel)
Liu J, Ma Z, Yan Y, Yuan D, Wang Y.
europepmc   +1 more source

Crater Observing Bioinspired Rolling Articulator (COBRA)

Advanced Intelligent Systems, EarlyView.
Crater Observing Bio‐inspired Rolling Articulator (COBRA) is a modular, snake‐inspired robot that addresses the mobility challenges of extraterrestrial exploration sites such as Shackleton Crater. Incorporating snake‐like gaits and tumbling locomotion, COBRA navigates both uneven surfaces and steep crater walls.
Adarsh Salagame, Henry Noyes, Eric Sihite, Arash Kalantari, Alireza Ramezani   +4 more
wiley   +1 more source

Influence of Post-Processing on S-Phase Formation During Plasma Nitriding of Additively Manufactured Inconel 939. [PDF]

Materials (Basel)
Maj P, Radziejewska J, Diduszko R, Marczak M, Nowicki R, Anna PL, Borowski T, Sitek R.   +7 more
europepmc   +1 more source

Loss‐Based Ensemble Generative Adversarial Network Model for Enhancing the Sperm Morphology Classification

Advanced Intelligent Systems, EarlyView.
A loss‐based ensemble generative adversarial network (GAN) framework is proposed to address mode collapse in sperm morphology classification. By integrating spatial augmentation and multiple GAN models, the study enhances synthetic data quality. The Shifted Window Transformer achieves 95.37% accuracy on the HuSHeM dataset, outperforming previous ...
Berke Cansiz, Hamza Osman Ilhan, Gorkem Serbes   +2 more
wiley   +1 more source