Results 151 to 160 of about 141,196 (301)

Sparse Pd–Te Covalent Bridges Drive Anomalous Bulk‐to‐Monolayer Electronic and Magnetic Evolution in FePd2Te2

Advanced Science, EarlyView.
Bulk FePd2Te2 contains sparse interlayer Pd–Te covalent bonds, giving it unexpectedly low exfoliation energy and enabling van der Waals‐like exfoliation. Cleaving these bonds during exfoliation makes the monolayer magnetically distinct from the bulk: magnetic anisotropy energy increases, and the strain‐response coefficient of the magnetic moment ...
Huaiyuan Zhao, Jianwen Fang, Mohan Luo, Yinuo Ye, Yulin Wang, Jianhui Yang, Hongwen Zhang, Peng Cheng   +7 more
wiley   +1 more source

Surrogate assisted optimization of decentralized wastewater facilities in severe cold regions. [PDF]

Sci Rep
Su Y, Gao X, Zheng Z.
europepmc   +1 more source

Machine Learning‐Assisted KCl‐CaCl2‐LiCl Electrolyte Design for Low‐Temperature, High‐Performance Calcium‐Based Liquid Metal Batteries

Advanced Science, EarlyView.
A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou, Lei Huang, Yan Zhou, Xiaohui Ning   +3 more
wiley   +1 more source

Computational Estimation of Temperature-Dependent Gas Entropy of Hydrogen Isotopologues. [PDF]

ACS Omega
Stratton BJ, Yildirim E, Middleburgh SC.
europepmc   +1 more source

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

Advanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su, Yanda Zhu, Hao Peng, Manman Li, Xuzihan Zhang, Meixiao Wu, Siyuan Zhang, Yuhan Zeng, Jiwen Liao, Ming Luo, Hetaishan Huang, Yutong Wang, Sean Li, Wenxian Li   +13 more
wiley   +1 more source

Configurational Entropy and Phase Stability in Lead-Free Mixed-Halide CsSn(Br_<i>x</i>I_1-<i>x</i>)₃. [PDF]

J Phys Chem Lett
Liu X, Wang B, Gong J, Chen X, Cai Y.
europepmc   +1 more source

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

Entropy Generation Optimization in Multidomain Systems: A Generalized Gouy-Stodola Theorem and Optimal Control. [PDF]

Entropy (Basel)
Richter H, Fathizadeh M, Kaptain T.
europepmc   +1 more source

Large Language Model‐Informed Dual‐Track AI Framework for the Synergistic Design of Crack‐Free and High‐Strength Superalloys

Advanced Science, EarlyView.
This paper illustrates a knowledge‐augmented dual‐track AI framework for advanced superalloy design. First, Large Language Models translate metallurgical heuristics into explicit rules to rapidly prune a vast compositional search space. Subsequently, LLM‐distilled priors safely guide a reinforcement learning agent during autonomous process optimization,
Jian Yao, Junkang Wu, Jie Su, Fuzhu Wang, Zi Wang, Li Wang, Liming Tan, Lan Huang, Feng Liu, Yong Liu   +9 more
wiley   +1 more source

Phase structure and machine learning identification in one dimensional systems with power law correlated disorder and long range hopping. [PDF]

Sci Rep
Pouranvari M.
europepmc   +1 more source