Results 181 to 190 of about 21,035 (244)

Benchmarking Large Language Models for Polymer Property Predictions

Macromolecular Rapid Communications, EarlyView.
Large language models (LLMs) are fine‐tuned on polymer thermal property datasets to directly predict glass transition, melting, and decomposition temperatures from SMILES inputs. Compared to state‐of‐the‐art models such as Polymer Genome, polyGNN, and polyBERT, LLMs achieve competitive yet lower accuracy.
Sonakshi Gupta, Akhlak Mahmood, Shivank Shukla, Rampi Ramprasad   +3 more
wiley   +1 more source

Smile-related oral characteristics in vietnamese students. [PDF]

Acta Odontol Latinoam
Pham TAV, Le PP, Nguyen PA.
europepmc   +1 more source

Role of High Fidelity Vs. Low Fidelity Experimental Data in Machine Learning Model Performance for Predicting Polymer Solubility

Macromolecular Rapid Communications, EarlyView.
The performance of machine learning models for classifying polymer solubility improves when a high‐fidelity experimental dataset is used compared to a low‐fidelity experimental dataset. This has important implications for justifying the value of spending additional time and resources preparing detailed experimental datasets.
Mona Amrihesari, Manali Banerjee, Raul Olmedo, Blair Brettmann   +3 more
wiley   +1 more source

Uncovering Key Characteristics of Antibacterial Peptides through Machine Learning

Macromolecular Rapid Communications, EarlyView.
Machine‐learning (ML) techniques using random forest classification models revealed key characteristics that predict effective antimicrobial peptides (AMPs) targeting Gram‐negative bacteria, Gram‐positive bacteria, and mycobacteria. The ideal cLogP (<$ < $−6) and net‐charge (≤+4) threshold was the same for all three targets with variations in the ...
Jooyoung Roh, Cyrille Boyer, Priyank V. Kumar   +2 more
wiley   +1 more source

Effects of varying anteroposterior craniodentofacial morphologies on three-dimensional smile variables. [PDF]

J Dent Sci
Banditsaowapak P, Cheng JH.
europepmc   +1 more source

Structure‐Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles

Macromolecular Rapid Communications, EarlyView.
This work presents a structure‐aware graph convolutional network that models polymers as statistical ensembles to predict macroscopic properties. By combining topologically realistic graphs generated via kinetic Monte Carlo simulations with explicit molar mass distributions, the framework achieves high accuracy in classifying architectures and ...
Julian Kimmig, Yannik Köster, Timo Koswig, Punith Raviswamy, Subhash V. S. Ganti, Stefan Zechel, Christopher Kuenneth, Ulrich S. Schubert   +7 more
wiley   +1 more source

Orthodontic Management of Vertical Maxillary Excess With Occlusal Cant and Molar Protraction in a Young Adult Female: A Case Report. [PDF]

Cureus
Mishra HA, Gaikwad S, Gangurde P, Sawant HR, Khedekar S.   +4 more
europepmc   +1 more source

Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers

Macromolecular Rapid Communications, EarlyView.
The performance of light‐emitting polymers emerges from coupled effects of chemical diversity, morphology, and exciton dynamics across multiple length scales. This Perspective reviews recent design strategies and experimental challenges, and discusses how machine learning can unify descriptors, data, and modeling approaches to efficiently navigate ...
Tian Tian, Yinyin Bao
wiley   +1 more source

Balanced Restoration: Optimizing Midface Rejuvenation Using Hyaluronic Acid Fillers. [PDF]

Plast Reconstr Surg Glob Open
Rohrich RJ, Cetto R, Gallagher CJ, Weir D, Hollander DA, Brown J, Wu K.   +6 more
europepmc   +1 more source