Molecular dynamics and Monte Carlo methods based on an atomistic description of matter are indispensable tools in modern science. Thanks to their ability of simulating the microscopic behavior of matter, they help interpret experimental results, provide insight, predict new phenomena, and replace costly or impossible experiments. Atomistic simulations find application in almost all areas of science and help design new drugs, discover new materials, find greener chemical processes, and address the energy problem.
The overall aim of the project is to extend the scope of atomistic simulations by removing one of its most significant limitations, namely the limited time scale that can be investigated in standard simulations. The methods that we have developed for this purpose are based on the construction of an external bias that is added to the Hamiltonian to remove kinetic bottlenecks. One first defines a set of collective coordinates that are functions of the atomic coordinates and then builds a bias that is a function of the collective coordinates.
The project was centered on the application of a variational principle to determine the bias. We were able to achieve all the methodological objectives of the project. This has allowed us to gain a deeper insight not only on the variational method but also on how collective variable based sampling methods work. This has led to the development of a novel, highly efficient and simple to use sampling method that we call OPES that is on the flight probability enhanced sampling. This has offered the possibility of putting many different and apparently unrelated sampling methods under the same umbrella. During the project powerful machine learning based methodologies have also been developed enhancing the power of our approach.
The development of method has gone hand in hand with applications in the area of chemical reactions, crystallization, and ligand-protein binding.
