The simplest method for obtaining LJ parameters is estimation from experimental correlations , as in the original CHARMM and GROMOS force fields. This method has a low computational overhead but very limited accuracy. Training LJ parameters against experimental properties is more computationally expensive, but became the predominant method in small molecule force fields, facilitated by the increase in computational power required to simulate those properties. This method has been used by many force fields, including OPLS , CGenFF , GAFF , and GROMOS . The dominant parameterization paradigm is to train the LJ parameters against liquid density ( ) and heat of vaporization (Δ ) measurements, as in the original OPLS parameterization by Jorgensen et al. . These two physical property targets are used because they are simple to calculate from simulation , are dependent on the molecular volume and attractive forces, and together constrain the LJ and parameters better than they do individually. Densities and enthalpies are related to the derivatives of free energy with respect to volume and temperature, respectively; accurately reproducing the free energy is the target for most force fields. We note that while this is the dominant choice, alternatives exist; notably, the GROMOS 53A6 and 2016H66 force fields both use solvation free energies in addition to and Δ . Additionally, ab initio calculations can be used to inform parameterization, for example, using rare-gas interaction energies and geometries to produce initial parameter estimates subsequently refined with physical property data . More recently, methods to produce LJ parameters entirely from ab initio data, using atom-in-molecule electron density partitioning , or the exchange hole dipole model have been proposed. Still, parameterization against small molecule and Δ is the dominant paradigm . Training against Δ in particular has some problematic aspects. Using fixed charge force fields, predictions of Δ require performing simulations in both liquid phase and gas phase, which means that the same parameters must capture two different polarization states to reproduce experimental measurements of Δ . There has been significant discussion on how to account for this polarization cost, which also arises in the calculation of hydration and solvation free energies . Methods suggested include calculating an explicit polarization cost or using semi-polarized charges , but the issue has not been definitively resolved. Additionally, some compounds, such as acids, can form clusters in the gas phase , which are not generally represented in gas phase simulations used to predict Δ . Another major issue is the availability of modern experimental Δ data. The NIST ThermoML Archive is the one of the largest open databases for physical property measurements, and contains roughly 500 total Δ data points, where a "data point" in this context is defined as an experimental measurement for a specific compound at a given temperature , pressure , and mole fraction . In contrast, the ThermoML Archive contains over 60,000 measurements of pure densities. The ThermoML Archive is certainly not the only location of Δ data (it lacks data prior to the year 2003, and many measurements of Δ date to the mid-20th century), but it is challenging to obtain uncertainty estimates , rigorous provenance , or fully computer-readable forms for these older measurements. This makes it difficult to systematically vet the experimental procedures and outputs when curating large scale datasets for parameter optimizations.