Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole-dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. This study enables the ability to predict the structure-property relations for PAN and analogues with sustainable ab initio accuracy across scales.

Mg3(Sb1-xBix)2 alloy has been extensively studied in the last 5 years due to its exceptional thermoelectric (TE) performance. The absence of accurate force field for inorganic alloy compounds presents great challenges for computational studies. Here, we explore the atomic microstructure, thermal, and elastic properties of the Mg3(Sb1-xBix)2 alloy at different solution concentrations through atomic simulations with a highly accurate machine learning interatomic potential (ML-IAP). We find atomic local ordering in the optimized structure with the Bi-Bi pair inclined to join adjacent layers and Sb-Sb pair preferring to stay within the same layer. The thermal conductivity changes with the solution concentrations can be correctly predicted through ML-IAP-based molecular dynamics simulations. Spectral thermal conductance analysis shows that the continuous movement of low-frequency peak to high frequency is responsible for the reduction of the thermal conductivity upon alloying. Elastic calculations reveal that similar to the thermal conductivity, solid solution alloying can reduce the overall elastic properties at both Mg3Sb2 and Mg3Bi2 ends, while anisotropic behavior is clearly observed with linear interpolation relationship upon alloying along the interlayer direction and nonlinearity along the intralayer direction. Although the atomic local ordering shows little effects on the properties of the Mg3(Sb1-xBix)2 alloy with only two alloying elements, it possesses potential important impacts on multiprincipal element inorganic TE alloys. This work provides a recipe for computational studies on the TE alloy systems and thus can accelerate the discovery and optimization of TE materials with high TE performance.