Mesoscale Simulation and Machine Learning of Asphaltene Aggregation Phase Behavior and Molecular Assembly Landscapes.

Molecular Simulation of Asphaltene Aggregation in Crude Oil by Monte Carlo Method

Nanoaggregation and Solubility of Crude Oil Asphaltenes from Molecular Dynamics Simulations

The aggregation and solubility of asphaltenes are studied by classical molecular dynamics simulations. Average three-dimensional atomistic models are built on the basis of experimental data for the asphaltenes from a series of crude oil samples. The simulation of two such asphaltene models in four different solvents puts into evidence the formation of oligomeric clusters and gives clues to the ...

Identification of RNA-binding sites in artemin based on docking energy landscapes and molecular dynamics simulation

There are questions concerning the functions of artemin, an abundant stress protein found in Artemiaduring embryo development. It has been reported that artemin binds RNA at high temperatures in vitro, suggesting an RNA protective role. In this study, we investigated the possibility of the presence of RNA-bindingsites and their structural properties in artemin, using docking energy ...

Machine learning assembly landscapes from particle tracking data.

Bottom-up self-assembly offers a powerful route for the fabrication of novel structural and functional materials. Rational engineering of self-assembling systems requires understanding of the accessible aggregation states and the structural assembly pathways. In this work, we apply nonlinear machine learning to experimental particle tracking data to infer low-dimensional assembly landscapes map...

Real-time Scheduling of a Flexible Manufacturing System using a Two-phase Machine Learning Algorithm

The static and analytic scheduling approach is very difficult to follow and is not always applicable in real-time. Most of the scheduling algorithms are designed to be established in offline environment. However, we are challenged with three characteristics in real cases: First, problem data of jobs are not known in advance. Second, most of the shop’s parameters tend to be stochastic. Third, th...