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Automatic Force Field Generation

by Zoe Greenburg

The accuracy of molecular dynamics (MD) simulations depends upon the quality of the force field used to parameterize the model. The development of these force fields is challenging to automate, due to the highly individual nature of each molecule and its environment that require uniquely programmed starting conditions.

Wang and co-workers recently addressed this issue using ForceBalance, in a JPCL paper titled ”Building Force Fields: An Automatic, Systematic, and Reproducible Approach”. Dr. Vijay Pande, the corresponding author of the paper and faculty at Stanford University explains, “Accurate force fields are critical for the success of molecular dynamics simulations, but yet due to the great challenges in building force fields, they have typically been made ‘by hand’, requiring many years of effort, and are not easily reproducible or produced in a systematic manner.” ForceBalance aims to create systematic and reproducible force fields by using a combination of experimental and theoretical data.

ForceBalance provides a hybrid approach that combines ab initio and experimental data

In order to find the parameters that allow a force field to accurately simulate a system, these parameters must be optimized to represent the physical properties of a system that are experimentally known, as well as what is known about the system from the theoretical calculations. Finally, the algorithm used to combine these two factors differs across force fields.

ForceBalance, an application developed in 2012 by Wang, which is available through, allows the researcher to improve the accuracy of the force field by basing the parameters on the data available, whether it be experimental, theoretical, or a combination of both.

Dr. David Case, from Rutgers University says “As I see it, the key new idea here is to use gradients of liquid state average properties to help drive the optimization procedure. This allows one to significantly expand that set of target properties that can be fit in an automated fashion. It doesn’t relieve one from having to choose and weight the targets that one hopes to fit.”

This approach greatly streamlines the process of force field creation, although the method is completely automatic. It still leaves the researchers room to manually alter aspects as they see fit.

ForceBalance maximizes the efficiency of force field development

Thermodynamic fluctuation formulas are employed to reduce the necessity of many long simulations. The simulations are only run for long periods of time when high precision is required, such as at the end of the optimization.

ForceBalance also combines several open source technologies that are available on the internet. OpenMM 6.0, the Work Queue Library, MBAR all contribute to a fast and accurate generation of force field parameters.

The TIP3P and TIP4P models were significantly improved with Force Balance

The TIP3P and TIP4P models are the most commonly used water molecules for the Amber and CHARMM force fields. TIP3P is used for many simulations that deal with biologically relevant phenomena. Thus, their further improvement is an exciting development for the field.

The original parameters developed for TIP4P were unable to reproduce the dielectric constant for water in simulations. However, once ForceBalance calculations were applied to the models, with starting points despite varying starting points, all converged to the same point. The data set included a wide range of temperatures and pressures for six different liquid properties. The newly optimized parameter set, called TIP4P-FB reproduces the dielectric constant of water reliably across wide ranges of temperature and pressure.

The same optimizations were made to the TIP3P model, as 3-point models of water are more widely used in research. The optimized parameters(TIP3P-FB) were similarly accurate to TIP4P-FB, except for slight deviations from the experimental data for the density of water at low temperatures, outside the range that is common in biomedically relevant simulations.

ForceBalance showcases that efficient, reproducible, and automatic force field generation is possible. The research team hopes to expand this method to more biologically relevant models.

Looking towards the future

This type of technology has many applications. Dr. Pande predicts that “By completely automating the parameterization of force fields, force field design can now be reproducible … and built systematically (we can build many force fields in a consistent manner, such that they would easily work with each other). It is our vision that there is a coming Renaissance in force field design, where there will be a considerable increase in force field accuracy due to this new approach”

gianniAutomatic Force Field Generation

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