A Transferable Polarizable Force Field for Urea Crystals and Aqueous Solutions

Assessment of GAFF and OPLS Force Fields for Urea: Crystal and Aqueous Solution Properties

Molecular simulations such as Monte Carlo, molecular dynamics, and metadynamics have been used to provide insight into crystallization phenomena, including nucleation and crystal growth. However, these simulations depend on the force field used, which models the atomic and molecular interactions, to adequately reproduce relevant material properties for the phases involved. Two widely used force fields, the General AMBER Force Field (GAFF) and the Optimized Potential for Liquid Simulations (OPLS), including several variants, have previously been used for studying urea crystallization. In this work, we investigated how well four different versions of the GAFF force field and five different versions of the OPLS force field reproduced known urea crystal and aqueous solution properties. Two force fields were found to have the best overall performance: a specific urea charge-optimized GAFF force field and the original all-atom OPLS force field. It is recommended that a suitable testing protocol involving both solution and solid properties, such as that used in this work, is adopted for the validation of force fields used for simulations of crystallization phenomena.

A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets

Accurate energy data from noncovalent interactions are essential for constructing force fields for molecular dynamics simulations of bio-macromolecular systems. There are two important practical issues in the construction of a reliable force field with the hope of balancing the desired chemical accuracy and working efficiency. One is to determine a suitable quantum chemistry level of theory for calculating interaction energies. The other is to use a suitable continuous energy function to model the quantum chemical energy data. For the first issue, we have recently calculated the intermolecular interaction energies using the SAPT0 level of theory, and we have systematically organized these energies into the ab initio SOFG-31 (homodimer) and SOFG-31-heterodimer datasets. In this work, we re-calculate these interaction energies by using the more advanced SAPT2 level of theory with a wider series of basis sets. Our purpose is to determine the SAPT level of theory proper for interaction energies with respect to the CCSD(T)/CBS benchmark chemical accuracy. Next, to utilize these energy datasets, we employ one of the well-developed machine learning techniques, called the CLIFF scheme, to construct a general-purpose force field for biomolecular dynamics simulations. Here we use the SOFG-31 dataset and the SOFG-31-heterodimer dataset as the training and test sets, respectively. Our results demonstrate that using the CLIFF scheme can reproduce a diverse range of dimeric interaction energy patterns with only a small training set. The overall errors for each SAPT energy component, as well as the SAPT total energy, are all well below the desired chemical accuracy of ~1 kcal/mol.

Intermolecular Non-Bonded Interactions from Machine Learning Datasets

Accurate determination of intermolecular non-covalent-bonded or non-bonded interactions is the key to potentially useful molecular dynamics simulations of polymer systems. However, it is challenging to balance both the accuracy and computational cost in force field modelling. One of the main difficulties is properly representing the calculated energy data as a continuous force function. In this paper, we employ well-developed machine learning techniques to construct a general purpose intermolecular non-bonded interaction force field for organic polymers. The original ab initio dataset SOFG-31 was calculated by us and has been well documented, and here we use it as our training set. The CLIFF kernel type machine learning scheme is used for predicting the interaction energies of heterodimers selected from the SOFG-31 dataset. Our test results show that the overall errors are well below the chemical accuracy of about 1 kcal/mol, thus demonstrating the promising feasibility of machine learning techniques in force field modelling.

Deep Eutectic Solvents: Molecular Simulations with a First-Principles Polarizable Force Field

The unique properties of deep eutectic solvents make them useful in a variety of applications. In this work we develop a first-principles force field for reline, which is composed of choline chloride and urea in the molar ratio 1:2. We start with the symmetry adapted perturbation theory (SAPT) protocol and then make adjustments to better reproduce the structure and dynamics of the liquid when compared to first-principles molecular dynamics (FPMD) simulations. The resulting force field is in good agreement with experiments in addition to being consistent with the FPMD simulations. The simulations show that primitive molecular clusters are preferentially formed with choline-chloride ionic pairs bound with a hydrogen bond in the hydroxyl group and that urea molecules coordinate the chloride mainly via the trans-H chelating hydrogen bonds. Incorporating polarizability qualitatively influences the radial distributions and lifetimes of hydrogen bonds and affects long-range structural order and dynamics. The polarizable force field predicts a diffusion constant about an order of magnitude larger than the nonpolarizable force field and is therefore less computationally intensive. We hope this study paves the way for studying complex hydrogen-bonding liquids from a first-principles approach.

CLIFF: A component-based, machine-learned, intermolecular force field

Computation of intermolecular interactions is a challenge in drug discovery because accurate ab initio techniques are too computationally expensive to be routinely applied to drug-protein models. Classical force fields are more computationally feasible, and force fields designed to match symmetry adapted perturbation theory (SAPT) interaction energies can remain accurate in this context. Unfortunately, the application of such force fields is complicated by the laborious parameterization required for computations on new molecules. Here, we introduce the component-based machine-learned intermolecular force field (CLIFF), which combines accurate, physics-based equations for intermolecular interaction energies with machine-learning models to enable automatic parameterization. The CLIFF uses functional forms corresponding to electrostatic, exchange-repulsion, induction/polarization, and London dispersion components in SAPT. Molecule-independent parameters are fit with respect to SAPT2+(3)δMP2/aug-cc-pVTZ, and molecule-dependent atomic parameters (atomic widths, atomic multipoles, and Hirshfeld ratios) are obtained from machine learning models developed for C, N, O, H, S, F, Cl, and Br. The CLIFF achieves mean absolute errors (MAEs) no worse than 0.70 kcal mol^-1 in both total and component energies across a diverse dimer test set. For the side chain-side chain interaction database derived from protein fragments, the CLIFF produces total interaction energies with an MAE of 0.27 kcal mol^-1 with respect to reference data, outperforming similar and even more expensive methods. In applications to a set of model drug-protein interactions, the CLIFF is able to accurately rank-order ligand binding strengths and achieves less than 10% error with respect to SAPT reference values for most complexes.

Why Lithium Ions Stick to Some Anions and Not Others

Designing battery electrolytes for lithium-ion batteries has been a topic of extensive research for decades. The ideal electrolyte must have a large conductivity as well as high Li⁺ transference number. The conductivity is very sensitive to the nature of the anions and dynamical correlations between ions. For example, lithium bis(trifluoromethane)sulfonimide (LiTFSI) has a large conductivity, but the chemically similar lithium trifluoromethanesulfonate (LiOTf) shows poor conductivity. In this work, we study the binding of Li⁺ to these anions in an ethylene carbonate (EC) solvent using enhanced sampling metadynamics. The evaluated free energies display a large dissociation barrier for LiOTf compared to LiTFSI, suggesting long-lived ion-pair formation in the former but not the latter. We probe these observations via unbiased molecular dynamics simulations and metadynamics simulations of TFSI with a hypothetical OTF-like partial charge model indicating an electrostatic origin for those differences. Our results highlight the deleterious impact of sulfonate groups in lithium-ion battery electrolytes and provide a new basis for the assessment of electrolyte designs.