Results 31 to 40 of about 2,775 (180)

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

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

Nanostructured Deep Eutectic Systems in Healthcare: From Bioactive Solvents to Intelligent Biointerfaces, Wearables, and AI‐Driven Design

Advanced NanoBiomed Research, EarlyView.
Beyond the green solvent paradigm, this review redefines Deep Eutectic Systems (DES) as programmable supramolecular nanoassemblies. We survey their biomedical convergence: stabilizing thermolabile mRNA to enable cold chain‐free logistics, reshaping transdermal microneedle delivery, enabling long‐term wearables via eutectogels, and utilizing Generative ...
Jeesu Moon, Min Seo Kim, Jae‐Seung Lee
wiley   +1 more source

Design Considerations for Polymer–Deep Eutectic Solvent Hybrid Systems for Transdermal Drug Delivery

Journal of Applied Polymer Science, EarlyView.
Deep eutectic solvents (DESs) act as tunable, green excipients that modulate polymer structure–property relationships to enhance drug solubility, skin permeation, stability, and controlled release. Through hydrogen‐bond interactions, DES–polymer hybrid systems enable improved transdermal performance, offering a rational, design‐driven platform for ...
Madhavi Kailas Kapale, Madhur Kulkarni, Pranav J. Shah, Furqan A. Maulvi   +3 more
wiley   +1 more source

The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential

Beilstein Journal of Nanotechnology
A key step in building regulatory acceptance of alternative or non-animal test methods has long been the use of interlaboratory comparisons or round-robins (RRs), in which a common test material and standard operating procedure is provided to all ...
Dimitra-Danai Varsou, Arkaprava Banerjee, Joyita Roy, Kunal Roy, Giannis Savvas, Haralambos Sarimveis, Ewelina Wyrzykowska, Mateusz Balicki, Tomasz Puzyn, Georgia Melagraki, Iseult Lynch, Antreas Afantitis   +11 more
doaj   +1 more source

Partial Least Square and Hierarchical Clustering in ADMET Modeling: Prediction of Blood – Brain Barrier Permeation of α-Adrenergic and Imidazoline Receptor Ligands

Journal of Pharmacy & Pharmaceutical Sciences, 2013
PURPOSE. Rate of brain penetration (logPS), brain/plasma equilibration rate (logPS-brain), and extent of blood-brain barrier permeation (logBB) of 29 α-adrenergic and imidazoline-receptors ligands were examined in Quantitative-Structure-Property ...
Katarina Nikolic, Slavica Filipic, Adam Smoliński, Roman Kaliszan, Danica Agbaba   +4 more
doaj   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

A QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIP (QSPR) EVALUATION OF CRITICAL VOLUME OF UNSATURATED HYDROCARBON ALKENES AND ALKYNES USING SIMPLE CONNECTIVITY INDICES

Reaktor, 2009
A quantitative structure-property relationships (QSPR) was used in this study to relate the critical volume (Vc) of unsaturated hydrocarbons alkenes and alkynes compounds to their molecular structures.
Oman Zuas, Dyah Styarini
doaj   +1 more source

QGeoSEP: A Novel Multi‐Task Learning Framework Integrating Quantum, Geometric, and Semantic Features for Collaborative Prediction of Multiple Properties With Potential Application to Energetic Materials

Materials Genome Engineering Advances, EarlyView.
We introduce QGeoSEP, a multi‐task learning framework for accurate energetic material property prediction, with competitive performance against mainstream baselines and an accessible online tool for efficient molecular evaluation. ABSTRACT Accurate physicochemical property prediction is critical for the rational design of energetic materials (EMs), yet
Mingchi Gao, Zhijiang Yang, Tengxin Huang, Yingjun Zhang, Liangliang Wang, Junjie Ding, Mingtian Li   +6 more
wiley   +1 more source

Using R topological indices for QSPR analysis of octanes

Main Group Metal Chemistry
Mathematical chemistry is the study of a chemical substance’s molecular structure as a graph and the use of computational methods and graph theory to mathematical problems. One important tool in this field that gives a network structure a numerical value
Denizler İsmail Hakkı, Çiftçi İdris
doaj   +1 more source