What is the cutting edge for open-source Force-Field generation?

Question

The adoption of atom-typing allowed for the creation of incredibly widely used force-fields (FF) such as GROMOS/AMBER/OPLS and many more. Because of the assumptions required in Molecular Mechanical (MM) models, in order to be accurate, at least part of the force-field must be fit to experimental data. This is very tedious, and there are many years, decades even, between updates to a FF.

Recent FF development has utilized chemical perception rather than atom-typing and seems to have alot of potential. For instance, the openFF consortium seems to be making large strides in creating an open source, easy to use and tinker with, FF.

Also, Parallel and GPU computing have come along way in the last decade. I would hope/assume that GPUs should make parameter optimization considerably faster than in previous decades.

Are there FF groups/projects in development that have the aim of being open source and highly "tinkerable", and are we in sight of being able to generate custom FFs somewhat on the fly, as we need them rather than relying on databases of atom-types fit nearly exclusively to solutes in water?

Answer 1

ForceBalance

ForceBalance is one approach which aims at making it easier to create force fields from a combination of theoretical and experimental data. I believe it's within the OpenMM framework.

The paper on the work can be found here https://pubs.acs.org/doi/10.1021/jz500737m

From the GitHub page it says

"The purpose of ForceBalance is to create force fields by applying a highly general and systematic process with explicitly specified input data and optimization methods, paving the way to higher accuracy and improved reproducibility."

The code can be downloaded here https://github.com/leeping/forcebalance

Answer 2

ForceBalance

Cody is right, ForceBalance is designed for the task of generating custom force fields. Force fields have a lot of variability in their functional forms, representation of parameters (as text, XML files or objects in code), the possible values or ranges of parameters, the software that runs simulations using the force field, the experimental data that the force field is fitted to, and so on. This means the workflow to produce a force field can vary depending on the needs of the project.

ForceBalance lets you carry out the force field optimization in a systematic and reproducible way by accommodating as much of this variability as possible in a single calculation. You can fit parameters to experimental properties (such as density of a liquid), ab initio properties (such as QM calculations of energy / forces for a set of configurations), or any linear combination. A number of properties are implemented, each of which can be computed using any of several software packages (Gromacs, AMBER, Tinker, OpenMM). FB is also used to fit the OpenFF small molecule parameter set in a manuscript we're currently writing.

Extension of ForceBalance to new types of data and simulation software can be done by writing new "target" or "engine" classes. It is available via the 3-clause BSD license. I'd be happy to answer more questions.