Results 161 to 170 of about 1,665,658 (262)

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source

Uncertainty Quantification for <i>In Silico</i> Chemistry. [PDF]

Chem Rev
Frömbgen T, Surzhikova E, Dölz J, Proppe J, Kirchner B, Jacob CR.   +5 more
europepmc   +1 more source

Dimensionality‐Driven Interfacial Electronic Coupling in Carbon‐Supported Gd‐CuO Catalysts for Selective CO2 Electroreduction to Ethylene

Advanced Functional Materials, EarlyView.
The 2D carbon‐supported Gd–CuO exhibits strong interfacial electronic coupling, abundant oxygen vacancies, and highly exposed active sites, enabling efficient CO enrichment and C─C coupling toward selective and durable CO2‐to‐C2H4 conversion. ABSTRACT Copper‐based catalysts are promising for electrochemical CO2 reduction toward value‐added chemicals ...
Xueya Zhu, Binghao Shen, Mengshan Chen, Yue Hu, Ruijie Xia, Senwei Ji, Zhi Teng, Donglin Xie, Shunli Li, Xue Zhao, Hermenegildo Garciad, Yingtang Zhou   +11 more
wiley   +1 more source

Decoding Uncertainty Quantification for Oncology-An Illustration Using Radiomics. [PDF]

Diagnostics (Basel)
van Daalen F, Sasidharan BK, Praveenraj C, Varghese AJ, Dekker A, Wee L, Fijten R, Irodi A, Thomas HMT.   +8 more
europepmc   +1 more source

Optimizing NiMo Electrocatalyst Coatings for Hydrogen Evolution in Alkaline Electrolysis With The Machine Learning‐Guided Autonomous Platform CatBot

Advanced Functional Materials, EarlyView.
This work presents a closed‐loop optimization workflow integrating CatBot, an automated synthesis and testing system, to discover NiMo hydrogen‐evolution catalysts. CatBot synthesised and evaluated 228 unique samples, identifying the best performing catalysts for transfer to nickel‐foam electrodes. The best candidate is highly active, reaching 1 Acm−2${
Paolo Vincenzo Freiesleben de Blasio, Jonas Elsborg, Elif Coşkun, Andreas Burger, Pradipkumar Manubhai Leuaa, Thu Pham Anh Phan, Jin Hyun Chang, Christodoulos Chatzichristodoulou   +7 more
wiley   +1 more source

Correction: Gated recurrent unit model for forecasting greenhouse gas concentrations with uncertainty quantification. [PDF]

Front Artif Intell
Kimei EH, Nyambo DG, Mduma N, Kaijage SF.   +3 more
europepmc   +1 more source

Amphoteric Cu‐Doping Leading to High Thermoelectric Performance in p‐Type Bismuth Antimony Telluride

Advanced Functional Materials, EarlyView.
This study shows that Cu plays two opposite roles in p‐type Bi0.5Sb1.5Te3 depending on the processing route. Under thermodynamic‐equilibrium bulk‐synthesis, Cu mainly occupies cation sites, increases hole concentration, preserves mobility, and slightly reduces lattice heat transport. The best‐performing composition among the investigated series reaches
Moritz Thiem, Zhiyuan Li, Minsu Heo, Fangyi Hu, Tian Lan, Youngdeog Koh, Ruiwen Xie, Chuanmu Tian, Marc Widenmeyer, Hyun‐Sik Kim, Jan P. Hofmann, Yuan Yu, Hongbin Zhang, Anke Weidenkaff, G. Jeffrey Snyder, Wenjie Xie   +15 more
wiley   +1 more source

The Physical Fidelity Gap as an Evidence-Traceability Problem in AI Uncertainty Quantification: A Structured Review. [PDF]

Sensors (Basel)
Guo L, Ma A, Zhou H, Liu Z, Xiong X.
europepmc   +1 more source

Nitride MXenes Beyond Carbides: Bridging the Gap Between Computational Prediction and Experimental Realization

Advanced Functional Materials, EarlyView.
Nitride MXenes remain constrained by a persistent gap between computational prediction and experimental realization. This Review identifies the thermodynamic, kinetic, and chemical barriers limiting their synthesis, critically evaluates emerging fabrication routes, and proposes a multidimensional computational‐experimental framework to accelerate the ...
Naresh Varnakavi, Masoud Soroush
wiley   +1 more source

Bayesian Uncertainty Quantification for A Fractional-Order Model of the Human Ear. [PDF]

J Assoc Res Otolaryngol
Kc P, Naghibolhosseini M, Zayernouri M.
europepmc   +1 more source