Results 221 to 230 of about 25,367 (309)

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 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

Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

Advanced Intelligent Discovery, EarlyView.
Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing, He Wang, Yangyang Sun, Xiaoquan Su, Shunyao Wu   +4 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

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 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

Limitations of Foundation Models in Energy Materials Simulations: A Case Study in Polyanion Sodium Cathode Materials

Advanced Intelligent Discovery, EarlyView.
Several simulation techniques are used to explore static and dynamic behavior in polyanion sodium cathode materials. The study reveals that universal machine learning interatomic potentials (MLIPs) struggle with system‐specific chemistry, emphasizing the need for tailored datasets.
Martin Hoffmann Petersen, Chiku Parida, Jonas Busk, Jin Hyun Chang, Arghya Bhowmik, Juan Maria Garcia Lastra   +5 more
wiley   +1 more source

Data-driven optimization of machining parameters for Hastelloy C276 using PSO and TLBO frameworks. [PDF]

Sci Rep
Abualhaj MM, Venkatesh B, Parmar KD, Mishra A, Arunkumar DT, Singh R, Mahapatro A, Bupesh Raja VK, Bhowmik A, Tamene Y.   +9 more
europepmc   +1 more source

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

The Influence of Operating Pressure Oscillations on the Machined Surface Topography in Abrasive Water Jet Machining. [PDF]

Materials (Basel)
Veljković DŽ, Baralić J, Janković P, Dučić N, Savković B, Jovičić A.   +5 more
europepmc   +1 more source