Results 61 to 70 of about 7,878 (217)

The rational design of polymeric EUV resist materials by QSPR modelling [PDF]

, 2007
We present the initial results of the development of a qualitative structure property relationship (QSPR) model to guide in the design and synthesis of high-sensitivity, non-CAR materials for EUV lithography.
Blakey, Idriss, Cao, Heidi, Denbeaux, Greg, Hill, David, Jack, Kevin, Leeson, Michael, Liu, Heping, Waterman, Justin, Whittaker, Andrew, Yueh, Wang   +9 more
core   +1 more source

Predicting Drop‐Weight Impact Sensitivity From Molecular Graphs Using Physics‐Informed Artificial Intelligence

Propellants, Explosives, Pyrotechnics, EarlyView.
This work presents an approach for predicting the drop‐weight impact sensitivity of pure molecular explosives directly from 2D molecular graphs using physics‐informed artificial intelligence (AI) models. A dataset comprising experimentally measured sensitivities for 625 unique high‐explosive molecules is augmented with physics‐informed synthetic ...
Grant Hutchings, Jack V. Davis, Virginia W. Manner, Marc J. Cawkwell, Frank W. Marrs   +4 more
wiley   +1 more source

Effect of Energy Harvesting on Stable Throughput in Cooperative Relay Systems

, 2015
In this paper, the impact of energy constraints on a two-hop network with a source, a relay and a destination under random medium access is studied.
Ephremides, Anthony, Jeon, Jeongho, Kountouris, Marios, Pappas, Nikolaos, Traganitis, Apostolos   +4 more
core   +1 more source

Unsupervised Hierarchical Symbolic Regression for Interpretable Property Modeling in Complex Multi‐Variable Systems

Advanced Science, Volume 13, Issue 19, 2 April 2026.
UHSR translates complex chemical behavior into clear and explainable equations. Applied to thin‐layer chromatography, it automatically uncovers the mathematical rules linking a molecule's structure to its polarity. This approach matches the accuracy of advanced AI while providing interpretable results, earning greater trust from chemists. The method is
Siyu Lou, Chengchun Liu, Dongxiao Zhang, Yuntian Chen, Fanyang Mo   +4 more
wiley   +1 more source

A QSPR MODEL FOR STEROIDS

Studia Universitatis Babes-Bolyai Chemia, 2016
A QSPR (Quantitative Structure-Property Relationship) model was derived for a set of forty 7 beta-hydroxysteroid compounds selected from PubChem database in order to assess the link between structural features and lipophilicity expressed as logP.
Lavinia L. PRUTEANU, Sara ERSALI, Sorana D. BOLBOACA   +2 more
doaj  

Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors.

PLoS ONE
Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests.
Mohamad Nazri Husin, Abdul Rauf Khan, Nadeem Ul Hassan Awan, Francis Joseph H Campena, Fairouz Tchier, Shahid Hussain   +5 more
doaj   +1 more source

High-RI resist polymers for 193 nm immersion lithography [PDF]

, 2005
A critical aim within the field of 193 nm immersion lithography is the development of high refractive index immersion fluids and resists. Increases in the refractive index (RI) of the immersion fluid will result in increases in the numerical aperture and
, Andrew K. Whittaker, David J. T. Hill, Graeme A. George, Heping Liu, Idriss Blakey, Paul Zimmerman, Will Conley   +7 more
core   +1 more source

QSPR Modeling using Catalan Solvent and Solute Parameters [PDF]

Journal of the Brazilian Chemical Society, 2011
A area de correlacao quantitativa entre estrutura e propriedade (QSPR) pode beneficiar-se de descritores moleculares que representam interacoes intermoleculares. Catalan desenvolveu um metodo de escalas solvatocromicas para solventes que pode ser explorado para esta finalidade.
Jouyban, Abolghasem, Fakhree, Mohammad A. A., Shayanfar, Ali, Ghafourian, Taravat   +3 more
openaire   +3 more sources

A Two‐Stage Machine Learning Framework With Physically Inspired Descriptors for Efficient Prediction of Hydration Free Energies

Chemistry–Methods, Volume 6, Issue 4, April 2026.
Hydration free energy is essential to understanding stability and reactivity in aqueous environments. Recently, we reported a machine learning model Mole2Solv to predict experimental hydration free energies of molecules (J. Phys. Chem. Lett., 2023, 14, 1877).
Luyang Jia, Zhan‐Yun Zhang, Bin‐Bin Xie, Lin Shen, Wei‐Hai Fang   +4 more
wiley   +1 more source

A Closed‐Loop Hybrid Discovery System of Type I Photosensitizers for Hypoxic Tumor Therapy

Advanced Science, Volume 13, Issue 9, 13 February 2026.
The work developed a closed‐loop hybrid discovery system to rationally design and predict high‐performance Type I PSs for hypoxic tumor therapy. 664 Potential candidates are identified from a dataset through a support vector machine (SVM) classification model, and two candidates are experimentally verified as Type I PSs, which highlighted the potential
Xia Ling, Yixin Zhu, Min Li, Zongliang Xie, Lei Cao, Wentao Song, Dandan Wang, Duo Mao, Xiaonan Wang, Bin Liu   +9 more
wiley   +1 more source