Results 211 to 220 of about 14,528 (304)

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

Investigation of surface subsidence behaviour in multi-slice longwall mining using numerical simulations in Barapukuria coal mine, Bangladesh. [PDF]

Sci Rep
Zhang C, Arif A, Wu J, Habibullah M, Mohammadamini K, Sajib MH, Manohar B.   +6 more
europepmc   +1 more source

Structure and Spectroscopic Characterisation of Phenanthroline‐Based Iodobismuthate(III) Complexes Utilised for Raw Acoustic Signal Classification

Advanced Intelligent Discovery, EarlyView.
Memristors based on trimethylsulfonium (phenanthroline)tetraiodobismuthate have been utilised as a nonlinear node in a delayed feedback reservoir. This system allowed an efficient classification of acoustic signals, namely differentiation of vocalisation of the brushtail possum (Trichosurus vulpecula).
Ewelina Cechosz, Gisya Abdi, Andrzej Sławek, Marlena Gryl, Konrad Szaciłowski   +4 more
wiley   +1 more source

Best-first search-based approach for mining top-k closed frequent itemsets from uncertain databases. [PDF]

PLoS One
Le N, Vo H, Nguyen T.
europepmc   +1 more source

Automation of Surgical Workflow Recognition: Unveiling the Surgical Instrument Kinematics that Underly Robot‐Assisted Prostatectomy Procedures

Advanced Intelligent Discovery, EarlyView.
Automated procedural analysis is recognized as one of the major game changers for robotic surgery. Meaning digital analysis needs to replace the manual assessments that set todays standard. Mechanical robotic‐instrument tracking enables the derivation of quantitative kinematic metrics that support behavior‐based workflow segmentation into distinct ...
Kateryna Pirkovets, Matthias N. van Oosterom, Vera A. Ottens, Henk G. van der Poel, Fijs W. B. van Leeuwen   +4 more
wiley   +1 more source

Upcycling Municipal Solid Incineration Fly Ash into Layered Double Hydroxide Nanomaterials: Heavy Metal Immobilization and Environmental Risk Assessment. [PDF]

Nanomaterials (Basel)
Zhao Y, Wang X, Zhang Z, Xu M.
europepmc   +1 more source

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Advanced Intelligent Discovery, EarlyView.
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley   +1 more source

Antimicrobial Secondary Metabolites From Rhizosphere-Associated Streptomyces Species in Northern Nigerian Agricultural Soils: Genomic Mining and Bioactivity Assessment. [PDF]

Environ Microbiol Rep
Zakari DA, Kareem SO, Mobolaji OT, Adeboye AO, Adebimpe AM.   +4 more
europepmc   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

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, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source