Toward Automated Benchmarking of Atomistic Force Fields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data Archive.
Atomistic molecular simulations are a powerful way to make quantitative predictions, but the accuracy of these predictions depends entirely on the quality of the force field employed. Although experimental measurements of fundamental physical properties offer a straightforward approach for evaluating force field quality, the bulk of this information has been tied up in formats that are not machine-readable. Compiling benchmark data sets of physical properties from non-machine-readable sources requires substantial human effort and is prone to the accumulation of human errors, hindering the development of reproducible benchmarks of force-field accuracy. Here, we examine the feasibility of benchmarking atomistic force fields against the NIST ThermoML data archive of physicochemical measurements, which aggregates thousands of experimental measurements in a portable, machine-readable, self-annotating IUPAC-standard format. As a proof of concept, we present a detailed benchmark of the generalized Amber small-molecule force field (GAFF) using the AM1-BCC charge model against experimental measurements (specifically, bulk liquid densities and static dielectric constants at ambient pressure) automatically extracted from the archive and discuss the extent of data available for use in larger scale (or continuously performed) benchmarks. The results of even this limited initial benchmark highlight a general problem with fixed-charge force fields in the representation low-dielectric environments, such as those seen in binding cavities or biological membranes.
Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center , New York, New York 10065, United States.
Kyle A
KA
Beauchamp
Julie M
JM
Behr
Ariën S
AS
Rustenburg
OpenEye Scientific Software Inc. , Santa Fe, New Mexico 87508, United States.
Christopher I
CI
Bayly
Thermodynamics Research Center, NIST , Boulder, Colorado 80305, United States.
Kenneth
K
Kroenlein
Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center , New York, New York 10065, United States.
John D
JD
Chodera
D001331:Automation; D004560:Electricity; D016247:Information Storage and Retrieval