From the talks and contributions it was clear there are common challenges that all researchers face in the coming years. In terms of the technologies available, the push to GPU computing, driven by machine learning, means that the software used for chemical simulations must be reassessed if they are going to exploit the potential computer power on offer.
Another key issue is how machine learning is used to design new chemical simulations. Machine learning, such as neural networks, or Gaussian process regression, can be trained to extract the underlying non-linear relationship between atomic positions and some property of the system (we are often looking for the relative energy differences for a given configuration of atoms, and the associated forces as these are used to drive the molecular dynamics simulation). The machine learning methods use for training data quantum mechanical simulations. The danger however, is that while the non-linear model is discovered, we retain no information about the true physics at play, which is critical if we wish to have a deeper understanding of the interactions at play within a simulation.
|The effect of d-electrons on atomic configurations is an|
electronic orbital effect and one that is not captured in
traditional force fields.
Transferability, data driven models, and physical driven models, ultimately are tied to how we partition the energy of a force field. By this we mean, how do we cut up the force field into different interactions, and how we even define these interactions. How do we define bonds? Can we accommodate reactivity into our models? What of electrostatic interactions? Should we use multipolar descriptions of charge?
Fitting and automation can be reliant on "wizardry". By that we mean, "to use a force field, and to design derivatives of it, how much highly specific expertise do we require - is the force field only really usable by those who designed it?" This is not ideal, as this slows the development of new models, and also hinders insight into why models work and fail for particular simulations.
Finally there is the topic of the reference data. More often than not, force fields are fitted to quantum mechanically generated reference data. We assume that this data is the "truth". Though, while many of the programs that perform quantum mechanical calculations have over time become closer in agreement with each other, they all share the same issue of accuracy. The fitting of force fields is thus a cyclic issue, where a reassessment of the training should be performed periodically.
Going forward the issues raised during the workshop will help inform CECAM and EU funding on future force field development and how it plays a critical role in computational chemistry, even in an age of high performance computing.
As organisers of the workshop, Colin, Chris and John would like to thank CECAM and CCP5 for the generous funding for the event, the invited speakers for their informative presentations, and the attendees whom we hope will have taken away new ideas and new future collaborations.