Results 121 to 130 of about 288,690 (232)

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

Experiment-Driven Gaussian Process Surrogate Modeling and Bayesian Optimization for Multi-Objective Injection Molding. [PDF]

Polymers (Basel)
Omar HM, Mukras SMS.
europepmc   +1 more source

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

Bayesian Optimization for Multicomponent Supramolecular Systems. [PDF]

J Am Chem Soc
Jansen SAH, Markvoort AJ, Graaf FV, Rutten MGTA, Dankers PYW, Vantomme G, de Greef TFA, Meijer EW.   +7 more
europepmc   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Validation of Dynamic Bayesian Optimization for a Non-Stationary Human-in-the-Loop Optimization Problem. [PDF]

IEEE Robot Autom Lett
Kim G, Sergi F.
europepmc   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Accelerated discovery of highly active enzyme nanohybrids with parallelized Bayesian optimization in hybrid space. [PDF]

Nat Commun
Liu Y, Hu H, Han Y, Deon Chon JS, Lo CL, Chen Z, Zhang Z, Yuan Z, Ge J.   +8 more
europepmc   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Navigating Ternary Doping in Li-ion Cathodes With Closed-Loop Multi-Objective Bayesian Optimization. [PDF]

Adv Mater
Zeinali Galabi N, Liu CH, Jain M, Kamel M, Jia S, Bengio Y, McCalla E.   +6 more
europepmc   +1 more source