Results 191 to 200 of about 241,960 (267)

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

Advanced Intelligent Discovery, EarlyView.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender, Marco Corrias, Cesare Franchini   +2 more
wiley   +1 more source

Phylogenetic Relationships of Five Phallales Species Based on Mitochondrial Genome Analysis. [PDF]

J Fungi (Basel)
Wang Y, Li D, Wang G, Guo Z, Wang X, Liu H.   +5 more
europepmc   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

Comparative genomic and phylogenetic analyses of <i>Crataegus</i> chloroplast genomes: insights for evolution and identification. [PDF]

Front Plant Sci
Sun X, Cui M, Zhao B, Wang Y, Zhang X, Liu Y.   +5 more
europepmc   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Integrative chloroplast omics in citrus: computational insights into genomic structural adaptation and phylogenetic relationships from 35 species. [PDF]

Front Plant Sci
Lin S, Shu J, Kang C, Fang W, Liang X, Xu K, Ma H.   +6 more
europepmc   +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, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Phylogenomic and evolutionary analysis of arrowhead (<i>Sagittaria</i> L.) chloroplast genomes. [PDF]

iScience
Zhao W, Gao F, Wei Y, Zhang Y, Xiang N, Zhang Q, Zou C, Duan Y, Zhou Y, Yuan T, Jiang F.   +10 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

Mitochondrial genomic datasets for 32 coral reef decapods from the Okinawa Archipelago. [PDF]

Data Brief
Yoshioka Y, Kanai M, Nagai T, Gishitomi S, Nagata T, Satoh N.   +5 more
europepmc   +1 more source