Results 201 to 210 of about 3,893 (298)

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

Harnessing genomic prediction in Brassica napus through a nested association mapping population. [PDF]

Plant Genome
Perumal S, Higgins EE, Sra S, Khedikar Y, Moore J, Chaudary R, Haile T, Koh CS, Vail S, Robinson SJ, Horner K, Hope B, Klein-Gebbinck H, Herrmann DT, Navabi K, Sharpe AG, Parkin IAP.   +16 more
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

Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery. [PDF]

Adv Neural Inf Process Syst
Yu Y, Liu N, Lu F, Gao T, Jafarzadeh S, Silling S.   +5 more
europepmc   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

Advanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

Unraveling the genetic basis of general combining ability in CIMMYT elite bread wheat germplasm: implications for breeding strategies optimization. [PDF]

Front Plant Sci
Saavedra-Ávila JI, Gerard GS, Esposito S, Govindan V, Huerta-Espino J, Tadesse Z, Dreisigacker S, Saint Pierre C, Pacheco A, Toledo F, Gardner KA, Crespo-Herrera L, Crossa J, Vitale P.   +13 more
europepmc   +1 more source

Reproducing Kernel Krein Spaces of Analytic Functions and Inverse Scattering

, 1985
Daniel Alpay
openalex   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Principal component guided DCGAN for mitigating mode collapse in electromagnetic pulse signal synthesis. [PDF]

Sci Rep
Gao Z, Zhao L, Zheng J, Yi T, Zhang C, Wang Q.   +5 more
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source