Peptoid polymers are often crystalline in the solid-state as examined by X-ray scattering, but thus far, there has been no attempt to identify a common structural motif among them. In order to probe the relationship between molecular structure and crystal structure, we synthesized and analyzed a series of crystalline peptoid copolymers, systematically varying peptoid side-chain length (S) and main-chain length (N). We also examined X-ray scattering data from 18 previously reported peptoid polymers. In all peptoids, we found that the unit cell dimensions, a, b, and c, are simple functions of S and N: a (Å) = 4.55, b (Å) = [2.98]N + 0.35, and c (Å) = [1.86]S + 5.5. These relationships, which apply to both bulk crystals and self-assembled nanosheets in water, indicate that the molecules adopt extended, planar conformations. Furthermore, we performed molecular dynamics simulations (MD) of peptoid polymer lattices, which indicate that all backbone amides adopt the cis conformation. This is a surprising conclusion, because previous studies on isolated molecules indicated an energetic preference for the trans conformer. This study demonstrates that when packed into supramolecular lattices or crystals, peptoid polymers prefer to adopt a regular, extended, all-cis secondary structure.

Polypeptoid homopolymers and block copolymers undergo thermal transitions in the solid state that can be detected by differential scanning calorimetry (DSC), but so far there is neither consensus on the underpinnings of the observed thermal transitions, nor consensus on the expected number of transitions. We synthesized a series of polypeptoid diblock copolymers containing hydrophobic alkyl sidechains and hydrophilic ethyleneoxide sidechains, systematically varying side-chain length (S), backbone mainchain length (N), block copolymer composition (n/m), and N-terminal group, 1 and studied their thermal transitions by a combination of X-ray scattering and DSC. The thermal transitions are largely unaffected by S, N, and n/m, but strongly affected by the N-terminal group. Block copolymers with an acetylated N-terminus exhibit two thermal transitions. The low temperature thermal transition is due to a transition from a crystalline phase to a sanidic liquid crystalline mesophase. The molecules adopt planar, board-like conformations and are arranged in a rectangular crystal lattice with extended backbones that run parallel to each other. The side-chains extend on either side and are located within the plane of the backbone. The liquid crystalline phase is characterized by conformational disorder in dimensions normal to the molecular plane. The high temperature thermal transition is due to melting of the liquid crystalline phase to give an isotropic phase. Block copolymers with a free N-terminus (non-acetylated) exhibit only one thermal transition, and similar out-of-plane conformational disorder. This disorder appears to be due to a difference in the pendant side chain display angle of the terminal nitrogen atom.

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

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.