Results 231 to 240 of about 15,379 (289)

Effect of Pre-Strain Induced Microstructure Evolution on Hydrogen Embrittlement Resistance of a CoCrNi Medium-Entropy Alloy. [PDF]

Materials (Basel)
Wang Z, Jing S, Yan Y.
europepmc   +1 more source

Recent Advances in Nano‐Microstructured Catalysts for Electrochemical Seawater Electrocatalysis

Advanced Energy Materials, EarlyView.
This review highlights advances in nano‐ and microstructured catalysts for electrochemical seawater conversion. It elucidates design principles, mechanistic understanding, and machine‐learning‐assisted discovery, and outlines key challenges and future opportunities toward efficient, selective, and durable seawater electrocatalysis.
Xiaodong Shao, Mengfang Liang, Xiaotong Han, Xu Yu, Huan Pang, Ho Seok Park   +5 more
wiley   +1 more source

Interface Engineering Suppresses Self-Annealing in Electroplated Nanograined Copper for Low-Temperature Copper-to-Copper Bonding. [PDF]

Adv Sci (Weinh)
Peng G, Dong X, Li B, He C, Chen C, Huang L, Liu J, Zhou Y, Li Q, Gong H, Wang Y, Mu K, Cherng SJ, Yang PY, Lu Y, Yang Y, Hung SW, Feng SP.   +17 more
europepmc   +1 more source

Deciphering Intricacies in Directional CO2 Conversion From Electrolysis to CO2 Batteries

Advanced Energy Materials, EarlyView.
This review will delve into the inherent connections and distinctions of CO2‐directed conversion in ECO2RR and CO2 batteries, in terms of product types, catalyst selection, catalytic mechanisms, and electrochemical performances, while proposing a benchmarking framework for the evaluation of CO2 batteries and innovative CO2 battery configurations for ...
Changfan Xu, Huaping Zhao, Lizhuang Chen, Weidong Shi, Guosheng Shao, Yong Lei   +5 more
wiley   +1 more source

Data-Driven Design and Fabrication of Heat-Resistant, Ultrastrong, Lightweight Aluminum-Based Entropy Alloy by Additive Manufacturing. [PDF]

Adv Sci (Weinh)
Wang E, Ding C, Zhou D, Xu C, Sing SL, Jiang J, Wu H.   +6 more
europepmc   +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

Texture and Flexural Fatigue Resistance Governed by Surface-Dependent Deformation and Recrystallization in the Copper Foils. [PDF]

Nanomaterials (Basel)
Wu T, Liu G, Liu D, Wang B, Wang B, Tian Y.   +5 more
europepmc   +1 more source

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

Advanced 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, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Harnessing strengthening-metastability synergy for extreme work hardening in additively manufactured titanium alloys. [PDF]

Nat Commun
Chen X, Xie Y, Zhang T, Jiang Z, Bai X, Xu S, Lu J.   +6 more
europepmc   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source