output
20142024
most citedGraph Neural Networks for Surfactant Multi-Property Prediction

26 citations

5 papers

math.AP2024★ 3 cited

Relaxing the sharp density stratification and columnar motion assumptions in layered shallow water systems

Mahieddine Adim, Roberta Bianchini, Vincent Duchêne

We rigorously justify the bilayer shallow-water system as an approximation to the hydrostatic Euler equations in situations where the flow is density-stratified with close-to-piece…

physics.chem-ph2024★ 20 cited

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks

Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya +3

The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in industry. Recently, classical QSPR and Graph Neural Network…

physics.chem-ph2024★ 26 cited

Graph Neural Networks for Surfactant Multi-Property Prediction

Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya +3

Surfactants are of high importance in different industrial sectors such as cosmetics, detergents, oil recovery and drug delivery systems. Therefore, many quantitative structure-pro…

physics.flu-dyn2023★ 2 cited

Simulation of magnetohydrodynamic flows of liquid metals with heat transfer or magnetic stirring

Shashwat Bhattacharya, Seyed Loghman Sanjari, Dmitry Krasnov +1

We discuss the effects of nonhomogeneous magnetic fields in liquid metal flows in two different configurations. In the first configuration, we briefly report the impact of fringing…

physics.ins-det2014★ 3 cited

The magnetic shielding for the neutron decay spectrometer aSPECT

Gertrud Konrad, Fidel Ayala Guardia, Stefan Baeßler +6

Many experiments in nuclear and neutron physics are confronted with the problem that they use a superconducting magnetic spectrometer which potentially affects other experiments by…