Results 171 to 180 of about 41,060,851 (256)

Generative and Experimental Validation of High Refractive Index Polymers via Domain Knowledge Approach with Small Data

Advanced Intelligent Discovery, EarlyView.
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo, Seigo Watanabe, Hana Matsunaga, Kenichi Oyaizu   +3 more
wiley   +1 more source

Differentiating between data-mining and text-mining terminology

South African Journal of Information Management, 2004
J.H. Kroeze, M.C. Matthee, T.J.D. Bothma
doaj   +1 more source

The role of AI in combating misinformation: leveraging text mining and social networking analysis. [PDF]

Sci Rep
Rohan A, Islam MA, Yoshi AM, Islam MM, Kabir MF, Ahmed KR.   +5 more
europepmc   +1 more source

In Situ Contact Angle Measurement for Autonomous Spin Coating in Self‐Driving Labs

Advanced Intelligent Discovery, EarlyView.
A vision‐based add‐on transforms commercial spin coaters into autonomous modules of Self‐Driving Labs. Combining a width‐scaled U‐Net with classical geometric analysis, the system simultaneously measures contact angles and estimates substrate pose using a single camera.
Sven Fischer, Micha Hiegle, Holger Röhm
wiley   +1 more source

BrajText-Saar: A structured cultural dataset for cultural text mining in indian heritage texts. [PDF]

Data Brief
Deshpande MM, Bafna PB.
europepmc   +1 more source

Artificial Intelligence‐Driven Network Pharmacology: A Methodological Paradigm Shift Bridging Traditional Wisdom and Modern Science

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence is redefining network pharmacology (NP). By integrating knowledge graph engineering, geometric deep learning, multiomics anchoring, and generative reasoning, AI‐driven NP (AI‐NP) transforms static target mapping into dynamic, predictive modeling.
Cong Wang, Sile Hu, Rigahai Ji, Riguna A, Siqige Hao, Ma Zhuo, Yi‐Hung Chen, Bisihalatu Hu, Ulana Bai, Xigui Hada   +9 more
wiley   +1 more source

Probing Machine Learning Interatomic Potentials on Ion Transport Properties

Advanced Intelligent Discovery, EarlyView.
We perform a systematic benchmark of six state‐of‐the‐art universal machine learning interatomic potentials on their ability to predict ion transport properties in lithium‐ and sodium‐based superionic conductors relevant to all‐solid‐state batteries.
Ogheneyoma Aghoghovbia, Ming Hu, Adji Bousso Dieng   +2 more
wiley   +1 more source

Fluorescent Hydrogel‐Based Strain Sensor With Machine Learning‐Augmented Performance

Advanced Intelligent Discovery, EarlyView.
Fluorescent hydrogel strain sensor based on carbon quantum dots enabling optical readout of deformation through strain‐dependent emission changes, coupled with Random Forest analysis to capture nonlinear fluorescence‐concentration relationships and identify optimal sensing conditions. Hydrogels are ideal matrices for bio‐integrated wearable sensors due
Tailai Chen, Tianlong Liu, Chao Lu, Rimsha Malik, Jin Zhang   +4 more
wiley   +1 more source

Accelerating the Discovery of Proton Conducting Electrolytes via Machine Learning‐Enabled Literature Mining

Advanced Intelligent Discovery, EarlyView.
An end‐to‐end knowledge discovery framework is established to automate high‐precision property extraction from small, specialized literature corpora. Utilizing a domain‐specific bidirectional encoder representation from a transformer model and data augmentation, the system accurately extracts and structures electrolyte performance data, ultimately ...
Gaheun Shin, Seongho Lee, Seulchan Kim, Ho‐Il Ji, Joonhee Kang   +4 more
wiley   +1 more source

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