Results 161 to 170 of about 79,268 (265)

Multiscale and Multi‐Timestep Switching of Multiple Machine Learning Force Fields for Artificial Intelligence‐Driven Materials Simulations

Advanced Intelligent Discovery, EarlyView.
Deep Potential model switching accelerates molecular dynamics by using a faster 4 Å model for most timesteps and periodically applying a high‐accuracy 6 Å model. Validation on solid TiO2 and liquid PEG shows preserved RDF correlations and stable NPT behavior, while NVE energy‐drift analyses identify cases requiring additional validation.
Ryuya Kanda, Megumu Yamazaki, Yuta Yoshimoto, Shintaro Izumi, Atsuki Inoue, Hiroshi Kawaguchi, Yasufumi Sakai   +6 more
wiley   +1 more source

Exploring the Role of Metabolites in Torsadogenic Risk Assessment: A Text-Mining Approach. [PDF]

Turk J Pharm Sci
Bilgin E, Tuğcu G, Aydın A.
europepmc   +1 more source

A Comprehensive Comparative Study of Active Learning Schemes for Nanophotonics Design

Advanced Intelligent Discovery, EarlyView.
Active learning (AL) strategies are benchmarked for the binary design of planar multilayer nanophotonic structures. Factorization machines combined with quantum annealing (QA) become effective as dimensionality increases. Hybrid QA provides the strongest results for 100‐layer problems, highlighting the importance of optimization method selection in ...
Serang Jung, Seongmin Kim, Jaehyun Park, Chaeyoung Park, Zhihao Xu, Sanghyo Hwang, Baiqing Ye, Youn sang Kim, Sun‐Kyung Kim, Tengfei Luo, Eungkyu Lee   +10 more
wiley   +1 more source

From Word2Vec to Transformers: Text‐Derived Composition Embeddings for Filtering Combinatorial Electrocatalysts

Advanced Intelligent Discovery, EarlyView.
With the downstream filter held fixed, text‐derived composition representations produce distinct screening outcomes across 14 electrocatalyst libraries. Embedding model and composition encoding control the trade‐off between candidate reduction and preservation of the best measured current.
Lei Zhang, Markus Stricker
wiley   +1 more source

Global distribution of isoprenoid quinones across Bacteria. [PDF]

mSystems
Chobert S-C, Kanwar S, Lerouxel O, Varoquaux N, Michaud J, Pelosi L, Pierrel F, Abby SS.   +7 more
europepmc   +1 more source

A Multistrategy AI‐Assisted Consensus Screening Pipeline for Discovery of Candidate p53 Monomer Binders

Advanced Intelligent Discovery, EarlyView.
An ultra‐fast small‐sample AI consensus pipeline combines co‐folding and docking to target structure‐lacking p53 monomers. Its optimal mini‐protein binder shows robust interfacial affinity and promising druggability, supplying unprecedented antitumor p53 templates.
Ningyao Li, Xueyi Liu, Qiuyue Li, Chengfeng Yue, Xin Li, Yugang Huang, Xin Deng, Junfei Zhang, Xue Liang, Guodong Ye   +9 more
wiley   +1 more source

Re-emerging Marburg virus disease in Africa: spillover ecology, geographic expansion, and surveillance vulnerabilities. [PDF]

Emerg Microbes Infect
Tibbo M, Barigye R, Ameni G.
europepmc   +1 more source

Current Standards of Monitoring Models in Healthcare Settings

Advanced Intelligent Discovery, EarlyView.
AI/ML‐enabled medical devices are entering clinical practice faster than monitoring standards mature. This review highlights gaps in postmarket surveillance, limited use of predetermined change‐control plans, and the need for ongoing performance tracking, drift detection, explainability, and workflow‐aware governance to support safer, more reliable ...
Alan Kay, Daljit Takher, Wenting Liu, Emily Wittrup, Gilbert S. Omenn, Kayvan Najarian   +5 more
wiley   +1 more source

When AI Starts Reading Battery Papers Like a Researcher. [PDF]

ACS Cent Sci
Lin DZ, Liu Y.
europepmc   +1 more source

Catalyst‐Specialized Chemical Language Model Based on Transformer Variational Autoencoder for Catalyst Design and Discovery

Advanced Intelligent Discovery, EarlyView.
We present CatTransVAE, a catalyst‐specialized chemical language model (CLM) built on a transformer variational autoencoder (VAE), developed through pretraining on general compounds followed by fine‐tuning on diverse catalyst databases. A template‐guided generation framework is introduced to enable controlled catalyst design under structural ...
Apakorn Kengkanna, Masahito Ohue
wiley   +1 more source