Computational Models

Sometimes the computational techniques at our disposal are not sufficient. This typically happens because a model needs to capture phenomena across many spatiotemporal scales, or study material compositions in a large design space. When needed, we expand our toolbox! Particularly, we focus on coarse-grained molecular dynamics and we use machine learning algorithms to develop new force-fields and efficiently sample combinations of molecular building blocks.

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Selected Publications

Computational Methods

2024

A Coarse Grained Molecular Dynamics Model for the Simulation of Lubricating Grease

Anthony benois, Sebastian Echeverri Restrepo, Nicola De Laurentis, Femke Hogenberk, Andrea Giuntoli, Piet M. Lugt

Tribology Letters (2024).

Computational Methods

14 Oct 2021

Systematic coarse-graining of epoxy resins with machine learning-informed energy renormalization

Andrea Giuntoli, Nitin K. Hansoge, Anton van Beek, Zhaoxu Meng, Wei Chen, and Sinan Keten

npj Comput Mater 7, 168 (2021).

Computational Methods

19 Mar 2021

Universal Relation for Effective Interaction between Polymer-Grafted Nanoparticles

Nitin K. Hansoge, Agam Gupta, Heather White, Andrea Giuntoli, and Sinan Keten*

Macromolecules 2021, 54, 3052–3064.