Mendoza Laboratory

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Force Field Development

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Machine Learning Force Field:

In the last several years, we have developed and implemented several different approaches to force fields using machine learning. The idea is to include also long interactions, charges, and forces, besides calculating energies. One promising direction is using Neural Nets, as well as Gaussian processes, to mention some.

First principles van der Waals Force Field

Using our accurate quantum mechanical calculations, we develop terms to capture the dispersion interactions between molecules and materials interaction with different gases. We have validated the FF with comparisons to the equation of state of each gas or by checking the isotherms for the material and molecules.

Force Field for molecular machines

We have started the development of force fields that can capture the interactions of molecular machines. The idea is similar to the electron force field where by putting an extra damping function for different electronic state we can capture the different postions for the moving molecule

Coarse Grained Force Field:

We have started the development of coarse grained force fields that can capture the relevant interactions at larger time scales (ms versus ps). The idea is to reduce the computer time and resources for MD.

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