Results 151 to 160 of about 862 (232)

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

Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives. [PDF]

Chempluschem
Vergadou N, Constantoudis V.
europepmc   +1 more source

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

Med Research, EarlyView.
The integration of AI/ML into nanomedicine offers a transformative approach to therapeutic design and optimization. Unlike conventional empirical methods, AI/ML models (such as classification, regression, and neural networks) enable the analysis of complex clinical and formulation datasets to predict optimal nanoparticle characteristics and therapeutic
Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, Ashish Kumar Agrawal   +3 more
wiley   +1 more source

Machine learning models for prediction of (Pro)cathepsin-glycosaminoglycan binding free energies based on molecular structure. [PDF]

Comput Struct Biotechnol J
Bojarski KK, Quoika PK, Zacharias M.
europepmc   +1 more source

Designing High‐Entropy Alloys With Low Stacking Fault Energy Through Interpretable Machine Learning

Materials Genome Engineering Advances, EarlyView.
In this study, we developed an interpretable machine learning (ML) ensemble framework and, by integrating the VEC criterion with the proposed machine learning scoring parameter in the alloy composition screening process, successfully designed multiple CoCrFeNiMn‐based HEAs with TWIP/TRIP effects and without the BCC phase.
Shuai Nie, Yixuan He, Haoxiang Liu, Xudong Liu, Haifeng Wang, Ziqing He, Menghao Yang   +6 more
wiley   +1 more source

LOCAL DIVERGENCE-FREE IMMERSED FINITE ELEMENT-DIFFERENCE METHOD USING COMPOSITE B-SPLINES. [PDF]

Adv Comput Sci Eng
Li L, Gruninger C, Lee JH, Griffith BE.
europepmc   +1 more source

Comprehensive Insights into Natural Bioactive Compounds: From Chemical Diversity and Mechanisms to Biotechnological Innovations and Applications

ChemistryOpen, EarlyView.
Natural products derived from plants, animals, fungi, bacteria, and minerals contain diverse bioactive classes such as alkaloids, flavonoids, terpenoids, saponins, tannins, and phenolics. These natural products work through different mechanisms, including ROS inhibition, NF‐κB suppression, and cytokine regulation, and exhibit wide applications across ...
Sajid Ali, Atif Ali Khan Khalil, Muhammad Saeed Akhtar, Adnan Amin, Wajid Zaman   +4 more
wiley   +1 more source

CModel: An Informer-Based Model for Robust Molecular Communication Signal Detection. [PDF]

Sensors (Basel)
Zhao W, Lu P, Sun H, Zhang P, Wang X.
europepmc   +1 more source

Universal Phase Identification of Block Copolymers From Physics‐Informed Machine Learning

Journal of Polymer Science, Volume 63, Issue 6, Page 1433-1440, 15 March 2025.
ABSTRACT Block copolymers play a vital role in materials science due to their diverse self‐assembly behavior. Traditionally, exploring the block copolymer self‐assembly and associated structure–property relationships involve iterative synthesis, characterization, and theory, which is labor‐intensive both experimentally and computationally.
Xinyi Fang, Elizabeth A. Murphy, Phillip A. Kohl, Youli Li, Craig J. Hawker, Christopher M. Bates, Mengyang Gu   +6 more
wiley   +1 more source

Maximum Entropy-Mediated Liquid-to-Solid Nucleation and Transition. [PDF]

J Chem Theory Comput
Dammann L, Kohns R, Huber P, Meißner RH.   +3 more
europepmc   +1 more source