Results 201 to 210 of about 53,776 (298)
Advanced Intelligent Discovery, EarlyView.Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar +3 more
wiley +1 more source
Measurement of Mood States Following Light Alcohol Consumption: Evidence from the Implicit Association Test. [PDF]
Behav Sci (Basel), 2018 Ito M, Matsuzaki N, Kawahara J.
europepmc +1 more source
Health of the Implicit Association Test at Age 3
, 2016 Anthony G. Greenwald, Brian A. Nosek
openalex +1 more source
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 +12 more
wiley +1 more source
Measuring implicit attitude toward ambiguity using Implicit Association Test.
The Proceedings of the Annual Convention of the Japanese Psychological Association, 2013 Takashi IDENO +2 more
openaire +2 more sources
Deep Learning‐Assisted Design of Mechanical Metamaterials
Advanced Intelligent Discovery, EarlyView.This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong +5 more
wiley +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 +4 more
wiley +1 more source
The validation of Implicit Association Test measures for smartphone and Internet addiction in at-risk children and adolescents. [PDF]
J Behav Addict, 2018 Roh D +5 more
europepmc +1 more source
Smartphone-Based Virtual and Augmented Reality Implicit Association Training (VARIAT) for Reducing Implicit Biases Toward Patients Among Health Care Providers: App Development and Pilot Testing [PDF]
Jiabin Shen +7 more
openalex +1 more source