Our Research and Development
Our interests are deeply rooted in studying the unknown and many of our team members have strong academic ties.
Please visit our ResearchGate profiles for a complete overview: David Zhe Gao / Filippo Federici Canova
Efficient Parameterization of Complex Molecule Surface Force Fields
We present an efficient scheme for parametrizing complex molecule surface force fields from ab initio data. The cost of producing a sufficient fitting library is mitigated using a 2D periodic embedded slab model made possible by the quantum mechanics/molecular mechanics scheme in CP2K. These results were then used in conjunction with genetic algorithm (GA) methods to optimize the large parameter sets needed to describe such systems. The derived potentials are able to well reproduce adsorption geometries and adsorption energies calculated using density functional theory. These techniques allow us to study a wide variety of flexible functional molecules with the aim to design and control the properties of their self assembled films.
Journal of Computational Chemistry 2015, 36, 1187-1195.
Advanced Materials Interfaces 2014, 1, 1400414.
Journal of Physical Chemistry C 2016, 120, 3913-3921.
Journal of Physical Chemistry C 2017, 121, 4393-4403.
Clustered Bayesian Neural Networks for Modeling Friction Processes
Friction and wear are the source of every mechanical device failure, and lubricants are essential for the operation of the devices. These physical phenomena have a complex nature so that no model capable of accurately predicting the behavior of lubricants exists. We use a machine learning (ML) method that infers the relationship between chemical composition of lubricants and their performance from a database. The fluid friction relation is modeled by a Bayesian neural network (BNN) which is trained to reproduce the results for a training set of fluids.
Journal of Chemical Theory and Computation 2017, 13, 3-8.