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1. Interaction energy of a pair of identical coplanar uniformly charged nanodisks

   https://doi.org/10.1063/1.5025336  

   Ciftja, Orion; Berry, Isaac (March 2018, AIP Advances)

   We consider a nanosystem consisting of two coplanar uniformly charged nanodisks that are coupled via Coulomb forces. Such a model represents a typical situation encountered in two-dimensional semiconductor quantum dot systems of electrons. We provide an exact integral expression for the interaction energy between the two coplanar nanodisks as a function of their separation distance. It is found that the difference between a standard Coulomb potential and the current one has features reminiscent of a Lennard-Jones interaction potential. The results derived can be useful to understand formation of clusters and/or aggregates in systems of coplanar charged nanodisks that contain electrons. 

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2. A neural network-assisted open boundary molecular dynamics simulation method

   https://doi.org/10.1063/5.0083198  

   Floyd, J. E.; Lukes, J. R. (May 2022, The Journal of Chemical Physics)

   A neural network-assisted molecular dynamics method is developed to reduce the computational cost of open boundary simulations. Particle influxes and neural network-derived forces are applied at the boundaries of an open domain consisting of explicitly modeled Lennard-Jones atoms in order to represent the effects of the unmodeled surrounding fluid. Canonical ensemble simulations with periodic boundaries are used to train the neural network and to sample boundary fluxes. The method, as implemented in the LAMMPS, yields temperature, kinetic energy, potential energy, and pressure values within 2.5% of those calculated using periodic molecular dynamics and runs two orders of magnitude faster than a comparable grand canonical molecular dynamics system. 

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3. Non-bonded force field model with advanced restrained electrostatic potential charges (RESP2)

   https://doi.org/10.1038/s42004-020-0291-4  

   Schauperl, Michael; Nerenberg, Paul S.; Jang, Hyesu; Wang, Lee-Ping; Bayly, Christopher I.; Mobley, David L.; Gilson, Michael K. (December 2020, Communications Chemistry)

   null (Ed.)

   Abstract The restrained electrostatic potential (RESP) approach is a highly regarded and widely used method of assigning partial charges to molecules for simulations. RESP uses a quantum-mechanical method that yields fortuitous overpolarization and thereby accounts only approximately for self-polarization of molecules in the condensed phase. Here we present RESP2, a next generation of this approach, where the polarity of the charges is tuned by a parameter, δ, which scales the contributions from gas- and aqueous-phase calculations. When the complete non-bonded force field model, including Lennard-Jones parameters, is optimized to liquid properties, improved accuracy is achieved, even with this reduced set of five Lennard-Jones types. We argue that RESP2 with δ  ≈ 0.6 (60% aqueous, 40% gas-phase charges) is an accurate and robust method of generating partial charges, and that a small set of Lennard-Jones types is a good starting point for a systematic re-optimization of this important non-bonded term. 

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4. Stockmayer fluid simulations for viscosity and glass transition temperature of ionic liquids

   https://doi.org/10.1063/5.0268727  

   Itliong, Jester N; Frischknecht, Amalie L; Stevens, Mark J; Nakamura, Issei (July 2025, The Journal of Chemical Physics)

   We develop a Stockmayer fluid model for molecular dynamics simulations of ionic liquids that captures molecular polarization, ionic conductivity, viscosity, and glass transition temperature, using ethylammonium nitrate (EAN) as an example. The ions in EAN are treated as spheres interacting via the Lennard-Jones potential with an embedded point charge and a permanent dipole moment. We show that our simulation results for EAN are consistent with experimental data and then explore the effects of the molecular parameters on the viscosity of ionic liquids. Our results indicate that viscosity monotonically increases with ionic charge and dipole moment but non-monotonically changes with ionic diameter (or molar volume). This non-monotonic trend arises from the competition among the electrostatic interactions, molecular packing, and size asymmetry between the cation and anion. Our model also shows that long-lived ion pairs result in higher viscosities. 

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5. Deep learning-based quasi-continuum theory for structure of confined fluids

   https://doi.org/10.1063/5.0096481  

   Wu, Haiyi; Aluru, N. R. (August 2022, The Journal of Chemical Physics)

   Predicting the structural properties of water and simple fluids confined in nanometer scale pores and channels is essential in, for example, energy storage and biomolecular systems. Classical continuum theories fail to accurately capture the interfacial structure of fluids. In this work, we develop a deep learning-based quasi-continuum theory (DL-QT) to predict the concentration and potential profiles of a Lennard-Jones (LJ) fluid and water confined in a nanochannel. The deep learning model is built based on a convolutional encoder–decoder network (CED) and is applied for high-dimensional surrogate modeling to relate the fluid properties to the fluid–fluid potential. The CED model is then combined with the interatomic potential-based continuum theory to determine the concentration profiles of a confined LJ fluid and confined water. We show that the DL-QT model exhibits robust predictive performance for a confined LJ fluid under various thermodynamic states and for water confined in a nanochannel of different widths. The DL-QT model seamlessly connects molecular physics at the nanoscale with continuum theory by using a deep learning model. 

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