AIggregate: A Machine Learning Approach for Classifying Micelle Shape-Reference-Cited by-同舟云学术

AIggregate: A Machine Learning Approach for Classifying Micelle Shape

Published:2022-09-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 12th Hellenic Conference on Artificial Intelligence
language:
Short-container-title:

Author:

Mertzios Alkiviadis¹,Papavasileiou Konstantinos²,Peristeras Loukas²,Giannakopoulos George³

Affiliation:

1. SKEL Lab, Institute of Informatics and Telecommunications, National Centre of Scientific Research Demokritos, Greece

2. Molecular Thermodynamics and Modelling of Materials Laboratory, Institute of Nanoscience and Nanotechnology, National Centre of Scientific Research Demokritos, Greece

3. SKEL Lab, Institute of Informatics and Telecommunications, National Centre of Scientific Research Demokritos, Greece and SciFY PNPC, Greece

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3549737.3549799

Reference29 articles.

1. AIggregate. 2022. AIggregate: A Machine Learning Approach for Classifying Micelle Shape. https://github.com/alkiviadis-mertzios/AIggregate AIggregate. 2022. AIggregate: A Machine Learning Approach for Classifying Micelle Shape. https://github.com/alkiviadis-mertzios/AIggregate

2. Promising Route for the Development of a Computational Framework for Self-Assembly and Phase Behavior Prediction of Ionic Surfactants Using MARTINI;Anogiannakis D.;Pure and Applied Chemistry,2020

3. 3D Semantic Parsing of Large-Scale Indoor Spaces

4. Cubic Mesomorphic Phases;Balmbra R.;Pure and Applied Chemistry,1969

5. Magnus L. Bergström . 2016. Second CMC in surfactant micellar systems. Pure and Applied Chemistry 22 (April 2016 ), 46–50. https://doi.org/10.1016/j.cocis.2016.02.008 10.1016/j.cocis.2016.02.008 Magnus L. Bergström. 2016. Second CMC in surfactant micellar systems. Pure and Applied Chemistry 22 (April 2016), 46–50. https://doi.org/10.1016/j.cocis.2016.02.008