Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large disparity in the time scales of atomistic and ensemble motion. Coarse-grained models are therefore used in place of costly all-atom simulations, accessing longer time scales and larger systems. Nevertheless, constructing the many-body potential of mean force that defines the structure and dynamics of a coarse-grained system can be complicated and computationally intensive. Machine learning shows great promise for the linked challenges of dimensionality reduction and learning the potential of mean force. To improve the coarse-graining of ILs, we present a neural network model trained on all-atom classical molecular dynamics simulations. The potential of mean force is expressed as two jointly trained neural network interatomic potentials that learn the coupled short-range and many-body long range molecular interactions. These interatomic potentials treat temperature as an explicit input variable to capture its influence on the potential of mean force. The model reproduces structural quantities with high fidelity, outperforms the temperature-independent baseline at capturing dynamics, generalizes to unseen temperatures, and incurs low simulation cost.

Biodegradable polymers are increasingly employed at the heart of therapeutic devices. Particularly in the form of thin and elongated fibers, they offer an effective strategy for controlled release in a variety of biomedical configurations such as sutures, scaffolds, wound dressings, surgical or imaging probes, and smart textiles. So far however, the fabrication of fiber-based drug delivery systems has been unable to fulfill significant requirements of medicated fibers such as multifunctionality, adequate mechanical strength, drug loading capability, and complex release profiles of multiple substances. Here, a novel paradigm in the design and fabrication of microstructured biodegradable fibers with tailored mechanical properties and capable of predefined release patterns from multiple reservoirs is proposed. Different biodegradable polymers compatible with the scalable thermal drawing process are identified, and their release properties as thin films of various thicknesses in the fiber form are experimentally investigated and modeled. Multimaterial microstructured fibers with predictable complex release profiles of potentially different substances are then designed and fabricated. Moreover, the tunability of the mechanical properties via tailoring the drawing process parameters is demonstrated, as well as the ability to weave such fibers. This work establishes a novel platform for biodegradable microstructured fibers for applications in implants, sutures, wound dressing, or tissue scaffolds.

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Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by enhancing safety and enabling higher energy densities. However, SPEs suffer from significantly lower ionic conductivity than liquid and solid ceramic electrolytes, limiting their adoption in functional batteries. To facilitate more rapid discovery of high ionic conductivity SPEs, we developed a chemistry-informed machine learning model that accurately predicts ionic conductivity of SPEs. The model was trained on SPE ionic conductivity data from hundreds of experimental publications. Our chemistry-informed model encodes the Arrhenius equation, which describes temperature activated processes, into the readout layer of a state-of-the-art message passing neural network and has significantly improved accuracy over models that do not encode temperature dependence. Chemically informed readout layers are compatible with deep learning for other property prediction tasks and are especially useful where limited training data are available. Using the trained model, ionic conductivity values were predicted for several thousand candidate SPE formulations, allowing us to identify promising candidate SPEs. We also generated predictions for several different anions in poly(ethylene oxide) and poly(trimethylene carbonate), demonstrating the utility of our model in identifying descriptors for SPE ionic conductivity.