Learning pair interactions from experimental or simulation data is of great interest for molecular simulations. We propose a general stochastic method for learning pair interactions from data using differentiable simulations (DiffSim). DiffSim defines a loss function based on structural observables, such as the radial distribution function, through molecular dynamics (MD) simulations. The interaction potentials are then learned directly by stochastic gradient descent, using backpropagation to calculate the gradient of the structural loss metric with respect to the interaction potential through the MD simulation. This gradient-based method is flexible and can be configured to simulate and optimize multiple systems simultaneously. For example, it is possible to simultaneously learn potentials for different temperatures or for different compositions. We demonstrate the approach by recovering simple pair potentials, such as Lennard-Jones systems, from radial distribution functions. We find that DiffSim can be used to probe a wider functional space of pair potentials compared with traditional methods like iterative Boltzmann inversion. We show that our methods can be used to simultaneously fit potentials for simulations at different compositions and temperatures to improve the transferability of the learned potentials.

Polysiloxanes are versatile polymeric materials with widespread applications in industries ranging from electronics to biomedical devices because of their unique thermal and viscoelastic properties. Accurate molecular simulations of polysiloxanes are essential for understanding their broad applications from a microscopic perspective. However, the accuracy of these simulations is highly dependent on the quality of the force fields used. In this work, we present a comprehensive benchmark and development of force fields tailored for polydimethylsiloxane, which is one of the most widely used polysiloxane materials. Our focus is on their performance in predicting key thermophysical properties including density, heat capacities, isothermal compressibility, and transport properties such as viscosity and thermal conductivity. Experimental measurements are performed in parallel to rigorously validate simulation outcomes. Existing force fields for polydimethylsiloxane, including those derived for organic and inorganic systems, are systematically evaluated against experimental data to identify limitations in accuracy and transferability. Simulation results are compared extensively with experimental observations across a range of temperatures and pressures, revealing the strengths and shortcomings of these commonly utilized force fields for polydimethylsiloxane. Discrepancies between force field predictions and experimental measurements are analyzed in detail for thermodynamic and transport properties of polydimethylsiloxane. This benchmark study serves as a critical assessment of current force fields for polydimethylsiloxane and offers guidelines for their further development, enabling more reliable simulations of polysiloxane-based materials for various industrial applications.

Polymer nanocomposites (PNCs) benefit from the enhanced properties originating from the synergistic effects of nanoparticles (NPs) and a polymer matrix. However, the incorporation of NPs slows PNC relaxation, and particularly at high-particle-loading, the PNC melts become non-flowing because the interfacially adsorbed polymers bridge the particles, generating a kinetically quasi-permanent particle network. Herein, by introduction of bound polymer loops on the NP surfaces, we molecularly design a relaxation-enhanced PNC, where interfacial polymers adhering to the NP surfaces freely relaxed, enabling formation of a dynamic, loose particle network that facilitates flow of the PNC melts. The resultant molten high-NP-loading composites maintain fluid-like and low-viscosity dynamics, while the corresponding glassy materials possess enhanced toughness and strength. Thus, the demonstrated principle of preparing relaxation-enhanced PNCs by optimizing the way by which polymers are attached to the filler surface represents the state-of-the-art strategy for overcoming the tradeoff between the processability and mechanical performance of PNCs.