Results 171 to 180 of about 1,415,967 (341)

Compression-expansion cycles for toroidal fusion reactors

, 1974
George H. Miley
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

China's progress in developing fourth-generation nuclear reactors. [PDF]

Natl Sci Rev
Poo MM.
europepmc   +1 more source

Fusion reactor safety

, 2017
Stieglitz, Robert, Boccaccini, Lorenzo Virgilio, Hesch, Klaus   +2 more
openaire   +3 more sources

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

Advanced Intelligent Discovery, EarlyView.
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir, Miguel Hernández‐del‐Valle, Christina Schenk, De‐Yi Wang, Maciej Haranczyk   +4 more
wiley   +1 more source

Occupancy-Aware Neural Distance Perception for Manipulator Obstacle Avoidance in the Tokamak Vacuum Vessel. [PDF]

Sensors (Basel)
Li F, Chou W.
europepmc   +1 more source

Irradiation e ects in tungsten under fusion-reactor-relevant conditions

, 2019
Aleksi Zitting
openalex   +1 more source

Two conceptual designs of helical fusion reactor FFHR-d1A based on ITER technologies and challenging ideas

, 2017
A. Sagara, J. Miyazawa, H. Tamura, Tomoyo Tanaka, T. Goto, N. Yanagi, R. Sakamoto, S. Masuzaki, Hiroaki Ohtani   +8 more
semanticscholar   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Universal Catalyst Design Framework for Electrochemical Hydrogen Peroxide Synthesis Facilitated by Local Atomic Environment Descriptors

Angewandte Chemie, EarlyView.
A universal catalyst design framework integrating weighted atom‐centered symmetry function (wACSF) descriptors with machine learning accurately predicts adsorption energies for 2e− water oxidation reaction. Microkinetic modeling and experimental validation confirm the framework's universality, establishing a powerful paradigm for rational ...
Zhijian Liu, Yan Liu, Yuqi Zhang, Yeyu Deng, Zhong Zheng, Ruth Knibbe, Tianxiang Gao, Mingzhe Li, Ziye Wang, Bingqian Zhang, Xue Jia, Di Zhang, Heng Liu, Xuqiang Shao, Zhengyang Gao, Li Wei, Hao Li, Weijie Yang   +17 more
wiley   +2 more sources