Improving force field accuracy by training against condensed phase mixture properties

Developing a sufficiently accurate classical force field representation of molecules is key to realizing the full potential of molecular simulation as a route to gaining fundamental insight into a broad spectrum of chemical and biological phenomena. This is only possible, however, if the many complex interactions between molecules of different species in the system are accurately captured by the model. Historically, the intermolecular van der Waals (vdW) interactions have primarily been trained against densities and enthalpies of vaporization of pure (single-component) systems, with occasional usage of hydration free energies. In this study, we demonstrate how including physical property data of binary mixtures can better inform these parameters, encoding more information about the underlying physics of the system in complex chemical mixtures. To demonstrate this, we re-train a select number of the Lennard-Jones parameters describing the vdW interactions of the OpenFF 1.0.0 (Parsley) fixed charge force field against training sets composed of densities and enthalpies of mixing for binary liquid mixtures as well as densities and enthalpies of vaporization of pure liquid systems, and assess the performance of each of these combinations. We show that retraining against the mixture data almost universally improves the force field's ability to reproduce both pure and mixture properties, reducing some systematic errors that exist when training vdW interactions against properties of pure systems only.

Improving force field accuracy by training against condensed phase mixture properties

Developing a sufficiently accurate classical force field representation of molecules is key to realizing the full potential of molecular simulation as a route to gaining fundamental insight into a broad spectrum of chemical and biological phenomena. This is only possible, however, if the many complex interactions between molecules of different species in the system are accurately captured by the model. Historically, the intermolecular van der Waals (vdW) interactions have primarily been trained against densities and enthalpies of vaporization of pure (single-component) systems, with occasional usage of hydration free energies. In this study, we demonstrate how including physical property data of binary mixtures can better inform these parameters, encoding more information about the underlying physics of the system in complex chemical mixtures. To demonstrate this, we re-train a select number of the Lennard-Jones parameters describing the vdW interactions of the OpenFF 1.0.0 (Parsley) fixed charge force field against training sets composed of densities and enthalpies of mixing for binary liquid mixtures as well as densities and enthalpies of vaporization of pure liquid systems, and assess the performance of each of these combinations. We show that retraining against the mixture data almost universally improves the force field's ability to reproduce both pure and mixture properties, reducing some systematic errors that exist when training vdW interactions against properties of pure systems only.

Predicting Nanoparticle Uptake by Biological Membranes: Theory and Simulation

We describe a new approach which predicts the level of internalisation or complete wrapping of nanoparticles by liposomes in solution. It is based on a generalisation of elastic theory to nanoscale particles with physical property data obtained from atomistic and coarse-grained simulations. We apply this approach to determine the maximum number of nanoparticles of a given type that can be internalised by a given liposome and give examples of how our approach might be used to identify and/or design nanoparticles with different uptakes: New data that could be correlated with nanoparticle toxicity experiments . We briefly discuss the possibility of designing nanoscale separations process.

Predicting Nanoparticle Uptake by Biological Membranes: Theory and Simulation

We describe a new approach which predicts the level of internalisation or complete wrapping of nanoparticles by liposomes in solution. It is based on a generalisation of elastic theory to nanoscale particles with physical property data obtained from atomistic and coarse-grained simulations. We apply this approach to determine the maximum number of nanoparticles of a given type that can be internalised by a given liposome and give examples of how our approach might be used to identify and/or design nanoparticles with different uptakes: New data that could be correlated with nanoparticle toxicity experiments . We briefly discuss the possibility of designing nanoscale separations process.

Deep Mineral Exploration of the Jinchuan Cu–Ni Sulfide Deposit Based on Aeromagnetic, Gravity, and CSAMT Methods

The exploration of deep mineral resources is an important prerequisite for meeting the continuous demand of resources. The geophysical method is one of the most effective means of exploring the deep mineral resources with a large depth and a high resolution. Based on the study of the geological background, petrophysical properties, and aeromagnetic anomaly characteristics of the Jinchuan Cu–Ni sulfide deposit, which is famous throughout the world, this paper uses the widely used gravity, aeromagnetic, and CSAMT (controlled source audio-frequency magnetotellurics) methods with a complementary resolution to reveal the favorable prospecting position. In order to obtain better inversion results, the SL0 norm tight support focusing regularization inversion method is introduced to process the section gravity and aeromagnetic data of the mining area. By combining the results with CSAMT, it is found that the medium-low resistivity, high density, and the high magnetic anomaly areas near the structural belt can nicely correspond with the known ore-bearing rock masses in the mining area. At the same time, according to the geophysical exploration model and geological and physical property data, four favorable ore-forming prospect areas are delineated in the deep part of the known mining area.