An interpretable deep learning approach for designing nanoporous silicon nitride membranes with tunable mechanical properties.

The high permeability and strong selectivity of nanoporous silicon nitride (NPN) membranes make them attractive in a broad range of applications. Despite their growing use, the strength of NPN membranes needs to be improved for further extending their biomedical applications. In this work, we implement a deep learning framework to design NPN membranes with improved or prescribed strength values. We examine the predictions of our framework using physics-based simulations. Our results confirm that the proposed framework is not only able to predict the strength of NPN membranes with a wide range of microstructures, but also can design NPN membranes with prescribed or improved strength. Our simulations further demonstrate that the microstructural heterogeneity that our framework suggests for the optimized design, lowers the stress concentration around the pores and leads to the strength improvement of NPN membranes as compared to conventional membranes with homogenous microstructures.

Phase transformation path in Aluminum under ramp compression; simulation and experimental study.

We present a framework based on non-equilibrium molecular dynamics (NEMD) to reproduce the phase transformation event of Aluminum under ramp compression loading. The simulated stress-density response, virtual x-ray diffraction patterns, and structure analysis are compared against the previously observed experimental laser-driven ramp compression in-situ x-ray diffraction data. The NEMD simulations show the solid-solid phase transitions are consistent to experimental observations with a close-packed face-centered cubic (fcc) (111), hexagonal close-packed (hcp) structure (002), and body-centered cubic bcc (110) planes remaining parallel. The atomic-level analysis of NEMD simulations identifiy the exact phase transformation pathway happening via Bain transformation while the previous in situ x-ray diffraction data did not provide sufficient information for deducing the exact phase transformation path.

Coexistence of vitreous and crystalline phases of H2O at ambient temperature.

Formation of vitreous ice during rapid compression of water at room temperature is important for biology and the study of biological systems. Here, we show that Raman spectra of rapidly compressed water at greater than 1 GPa at room temperature exhibits the signature of high-density amorphous ice, whereas the X-ray diffraction (XRD) pattern is dominated by crystalline ice VI. To resolve this apparent contradiction, we used molecular dynamics simulations to calculate full vibrational spectra and diffraction patterns of mixtures of vitreous ice and ice VI, including embedded interfaces between the two phases. We show quantitatively that Raman spectra, which probe the local polarizability with respect to atomic displacements, are dominated by the vitreous phase, whereas a small amount of the crystalline component is readily apparent by XRD. The results of our combined experimental and theoretical studies have implications for detecting vitreous phases of water, survival of biological systems under extreme conditions, and biological imaging. The results provide additional insight into the stable and metastable phases of H2O as a function of pressure and temperature, as well as of other materials undergoing pressure-induced amorphization and other metastable transitions.